{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "BjP220de4M8A"
   },
   "source": [
    "## Import Libraries and Database"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "yP3PjLlP2NWI"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: wfdb in /Users/petercharlton/anaconda3/lib/python3.8/site-packages (4.1.0)\n",
      "Requirement already satisfied: numpy<2.0.0,>=1.10.1 in /Users/petercharlton/anaconda3/lib/python3.8/site-packages (from wfdb) (1.20.1)\n",
      "Requirement already satisfied: pandas<2.0.0,>=1.0.0 in /Users/petercharlton/anaconda3/lib/python3.8/site-packages (from wfdb) (1.2.4)\n",
      "Requirement already satisfied: requests<3.0.0,>=2.8.1 in /Users/petercharlton/anaconda3/lib/python3.8/site-packages (from wfdb) (2.25.1)\n",
      "Requirement already satisfied: matplotlib<4.0.0,>=3.2.2 in /Users/petercharlton/anaconda3/lib/python3.8/site-packages (from wfdb) (3.3.4)\n",
      "Requirement already satisfied: scipy<2.0.0,>=1.0.0 in /Users/petercharlton/anaconda3/lib/python3.8/site-packages (from wfdb) (1.6.2)\n",
      "Requirement already satisfied: SoundFile<0.12.0,>=0.10.0 in /Users/petercharlton/anaconda3/lib/python3.8/site-packages (from wfdb) (0.10.3.post1)\n",
      "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.3 in /Users/petercharlton/anaconda3/lib/python3.8/site-packages (from matplotlib<4.0.0,>=3.2.2->wfdb) (2.4.7)\n",
      "Requirement already satisfied: pillow>=6.2.0 in /Users/petercharlton/anaconda3/lib/python3.8/site-packages (from matplotlib<4.0.0,>=3.2.2->wfdb) (8.2.0)\n",
      "Requirement already satisfied: python-dateutil>=2.1 in /Users/petercharlton/anaconda3/lib/python3.8/site-packages (from matplotlib<4.0.0,>=3.2.2->wfdb) (2.8.1)\n",
      "Requirement already satisfied: cycler>=0.10 in /Users/petercharlton/anaconda3/lib/python3.8/site-packages (from matplotlib<4.0.0,>=3.2.2->wfdb) (0.10.0)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in /Users/petercharlton/anaconda3/lib/python3.8/site-packages (from matplotlib<4.0.0,>=3.2.2->wfdb) (1.3.1)\n",
      "Requirement already satisfied: six in /Users/petercharlton/anaconda3/lib/python3.8/site-packages (from cycler>=0.10->matplotlib<4.0.0,>=3.2.2->wfdb) (1.15.0)\n",
      "Requirement already satisfied: pytz>=2017.3 in /Users/petercharlton/anaconda3/lib/python3.8/site-packages (from pandas<2.0.0,>=1.0.0->wfdb) (2021.1)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in /Users/petercharlton/anaconda3/lib/python3.8/site-packages (from requests<3.0.0,>=2.8.1->wfdb) (2022.12.7)\n",
      "Requirement already satisfied: idna<3,>=2.5 in /Users/petercharlton/anaconda3/lib/python3.8/site-packages (from requests<3.0.0,>=2.8.1->wfdb) (2.10)\n",
      "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /Users/petercharlton/anaconda3/lib/python3.8/site-packages (from requests<3.0.0,>=2.8.1->wfdb) (1.26.4)\n",
      "Requirement already satisfied: chardet<5,>=3.0.2 in /Users/petercharlton/anaconda3/lib/python3.8/site-packages (from requests<3.0.0,>=2.8.1->wfdb) (4.0.0)\n",
      "Requirement already satisfied: cffi>=1.0 in /Users/petercharlton/anaconda3/lib/python3.8/site-packages (from SoundFile<0.12.0,>=0.10.0->wfdb) (1.14.5)\n",
      "Requirement already satisfied: pycparser in /Users/petercharlton/anaconda3/lib/python3.8/site-packages (from cffi>=1.0->SoundFile<0.12.0,>=0.10.0->wfdb) (2.20)\n",
      "Requirement already satisfied: neurokit2 in /Users/petercharlton/anaconda3/lib/python3.8/site-packages (0.2.3)\n",
      "Requirement already satisfied: numpy in /Users/petercharlton/anaconda3/lib/python3.8/site-packages (from neurokit2) (1.20.1)\n",
      "Requirement already satisfied: matplotlib in /Users/petercharlton/anaconda3/lib/python3.8/site-packages (from neurokit2) (3.3.4)\n",
      "Requirement already satisfied: pandas in /Users/petercharlton/anaconda3/lib/python3.8/site-packages (from neurokit2) (1.2.4)\n",
      "Requirement already satisfied: scikit-learn>=1.0.0 in /Users/petercharlton/anaconda3/lib/python3.8/site-packages (from neurokit2) (1.2.2)\n",
      "Requirement already satisfied: scipy in /Users/petercharlton/anaconda3/lib/python3.8/site-packages (from neurokit2) (1.6.2)\n",
      "Requirement already satisfied: threadpoolctl>=2.0.0 in /Users/petercharlton/anaconda3/lib/python3.8/site-packages (from scikit-learn>=1.0.0->neurokit2) (2.1.0)\n",
      "Requirement already satisfied: joblib>=1.1.1 in /Users/petercharlton/anaconda3/lib/python3.8/site-packages (from scikit-learn>=1.0.0->neurokit2) (1.2.0)\n",
      "Requirement already satisfied: pillow>=6.2.0 in /Users/petercharlton/anaconda3/lib/python3.8/site-packages (from matplotlib->neurokit2) (8.2.0)\n",
      "Requirement already satisfied: cycler>=0.10 in /Users/petercharlton/anaconda3/lib/python3.8/site-packages (from matplotlib->neurokit2) (0.10.0)\n",
      "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.3 in /Users/petercharlton/anaconda3/lib/python3.8/site-packages (from matplotlib->neurokit2) (2.4.7)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in /Users/petercharlton/anaconda3/lib/python3.8/site-packages (from matplotlib->neurokit2) (1.3.1)\n",
      "Requirement already satisfied: python-dateutil>=2.1 in /Users/petercharlton/anaconda3/lib/python3.8/site-packages (from matplotlib->neurokit2) (2.8.1)\n",
      "Requirement already satisfied: six in /Users/petercharlton/anaconda3/lib/python3.8/site-packages (from cycler>=0.10->matplotlib->neurokit2) (1.15.0)\n",
      "Requirement already satisfied: pytz>=2017.3 in /Users/petercharlton/anaconda3/lib/python3.8/site-packages (from pandas->neurokit2) (2021.1)\n"
     ]
    }
   ],
   "source": [
    "! pip install wfdb\n",
    "! pip install neurokit2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import neurokit2 as nk2\n",
    "import wfdb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "J_iR8w7j2VaV"
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from scipy import signal\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "id": "yqPlzYEf4TqQ"
   },
   "outputs": [],
   "source": [
    "# MIMIC info\n",
    "database_name = 'mimic3wdb/1.0' # The name of the MIMIC IV Waveform Database on Physionet\n",
    "\n",
    "# Segment for analysis\n",
    "segment_names = ['83404654_0005', '82924339_0007', '84248019_0005', \n",
    "                 '82439920_0004', '82800131_0002', '84304393_0001', \n",
    "                 '89464742_0001', '88958796_0004', '88995377_0001', \n",
    "                 '85230771_0004', '86643930_0004', '81250824_0005', \n",
    "                 '87706224_0003', '83058614_0005', '82803505_0017', \n",
    "                 '88574629_0001', '87867111_0012', '84560969_0001', \n",
    "                 '87562386_0001', '88685937_0001', '86120311_0001', \n",
    "                 '89866183_0014', '89068160_0002', '86380383_0001', \n",
    "                 '85078610_0008', '87702634_0007', '84686667_0002', \n",
    "                 '84802706_0002', '81811182_0004', '84421559_0005', \n",
    "                 '88221516_0007', '80057524_0005', '84209926_0018', \n",
    "                 '83959636_0010', '89989722_0016', '89225487_0007', \n",
    "                 '84391267_0001', '80889556_0002', '85250558_0011', \n",
    "                 '84567505_0005', '85814172_0007', '88884866_0005', \n",
    "                 '80497954_0012', '80666640_0014', '84939605_0004', \n",
    "                 '82141753_0018', '86874920_0014', '84505262_0010', \n",
    "                 '86288257_0001', '89699401_0001', '88537698_0013', \n",
    "                 '83958172_0001']\n",
    "\n",
    "segment_dirs = ['mimic4wdb/0.1.0/waves/p100/p10020306/83404654', \n",
    "                'mimic4wdb/0.1.0/waves/p101/p10126957/82924339', \n",
    "                'mimic4wdb/0.1.0/waves/p102/p10209410/84248019', \n",
    "                'mimic4wdb/0.1.0/waves/p109/p10952189/82439920', \n",
    "                'mimic4wdb/0.1.0/waves/p111/p11109975/82800131', \n",
    "                'mimic4wdb/0.1.0/waves/p113/p11392990/84304393', \n",
    "                'mimic4wdb/0.1.0/waves/p121/p12168037/89464742', \n",
    "                'mimic4wdb/0.1.0/waves/p121/p12173569/88958796', \n",
    "                'mimic4wdb/0.1.0/waves/p121/p12188288/88995377', 'mimic4wdb/0.1.0/waves/p128/p12872596/85230771', 'mimic4wdb/0.1.0/waves/p129/p12933208/86643930', 'mimic4wdb/0.1.0/waves/p130/p13016481/81250824', 'mimic4wdb/0.1.0/waves/p132/p13240081/87706224', 'mimic4wdb/0.1.0/waves/p136/p13624686/83058614', 'mimic4wdb/0.1.0/waves/p137/p13791821/82803505', 'mimic4wdb/0.1.0/waves/p141/p14191565/88574629', 'mimic4wdb/0.1.0/waves/p142/p14285792/87867111', 'mimic4wdb/0.1.0/waves/p143/p14356077/84560969', 'mimic4wdb/0.1.0/waves/p143/p14363499/87562386', 'mimic4wdb/0.1.0/waves/p146/p14695840/88685937', 'mimic4wdb/0.1.0/waves/p149/p14931547/86120311', 'mimic4wdb/0.1.0/waves/p151/p15174162/89866183', 'mimic4wdb/0.1.0/waves/p153/p15312343/89068160', 'mimic4wdb/0.1.0/waves/p153/p15342703/86380383', 'mimic4wdb/0.1.0/waves/p155/p15552902/85078610', 'mimic4wdb/0.1.0/waves/p156/p15649186/87702634', 'mimic4wdb/0.1.0/waves/p158/p15857793/84686667', 'mimic4wdb/0.1.0/waves/p158/p15865327/84802706', 'mimic4wdb/0.1.0/waves/p158/p15896656/81811182', 'mimic4wdb/0.1.0/waves/p159/p15920699/84421559', 'mimic4wdb/0.1.0/waves/p160/p16034243/88221516', 'mimic4wdb/0.1.0/waves/p165/p16566444/80057524', 'mimic4wdb/0.1.0/waves/p166/p16644640/84209926', 'mimic4wdb/0.1.0/waves/p167/p16709726/83959636', 'mimic4wdb/0.1.0/waves/p167/p16715341/89989722', 'mimic4wdb/0.1.0/waves/p168/p16818396/89225487', 'mimic4wdb/0.1.0/waves/p170/p17032851/84391267', 'mimic4wdb/0.1.0/waves/p172/p17229504/80889556', 'mimic4wdb/0.1.0/waves/p173/p17301721/85250558', 'mimic4wdb/0.1.0/waves/p173/p17325001/84567505', 'mimic4wdb/0.1.0/waves/p174/p17490822/85814172', 'mimic4wdb/0.1.0/waves/p177/p17738824/88884866', 'mimic4wdb/0.1.0/waves/p177/p17744715/80497954', 'mimic4wdb/0.1.0/waves/p179/p17957832/80666640', 'mimic4wdb/0.1.0/waves/p180/p18080257/84939605', 'mimic4wdb/0.1.0/waves/p181/p18109577/82141753', 'mimic4wdb/0.1.0/waves/p183/p18324626/86874920', 'mimic4wdb/0.1.0/waves/p187/p18742074/84505262', 'mimic4wdb/0.1.0/waves/p188/p18824975/86288257', 'mimic4wdb/0.1.0/waves/p191/p19126489/89699401', 'mimic4wdb/0.1.0/waves/p193/p19313794/88537698', 'mimic4wdb/0.1.0/waves/p196/p19619764/83958172']\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "aj261Ifa4eAg"
   },
   "source": [
    "# Key Functions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "wTtozKeh4uFd"
   },
   "source": [
    "All functions are derived from the original PPG work done by Elisa Mejía Mejía."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "id": "r23MUf6z4hRq"
   },
   "outputs": [],
   "source": [
    "start_seconds = 100 # time since the start of the segment at which to begin extracting data\n",
    "no_seconds_to_load = 60\n",
    "  \n",
    "rel_segment_no = 3\n",
    "rel_segment_name = segment_names[rel_segment_no]\n",
    "rel_segment_dir = segment_dirs[rel_segment_no]\n",
    "\n",
    "sig, meta = wfdb.rdsamp(record_name=rel_segment_name, \n",
    "                          pn_dir=rel_segment_dir, \n",
    "                          sampfrom=0, sampto=100)\n",
    "fs = round(meta['fs'])\n",
    "\n",
    "sampfrom = fs*start_seconds\n",
    "sampto = fs*(start_seconds+no_seconds_to_load)\n",
    "\n",
    "ppg, fields = wfdb.rdsamp(record_name=rel_segment_name, \n",
    "                          pn_dir=rel_segment_dir, \n",
    "                          sampfrom=sampfrom, sampto=sampto, \n",
    "                          channel_names = ['Pleth']) \n",
    "\n",
    "ppg = ppg.squeeze()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "bSNntepJ4MZU"
   },
   "source": [
    "## Find peaks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "id": "PhBgJ7Of2XUd"
   },
   "outputs": [],
   "source": [
    "def get_peaks(raw, fs):\n",
    "  ppg_clean = nk2.ppg_clean(raw, sampling_rate=fs)\n",
    "  peaks = nk2.ppg_findpeaks(ppg_clean, method=\"bishop\", show=False)  \n",
    "  return peaks['PPG_Peaks'][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "id": "sopAfwzH4K0r"
   },
   "outputs": [],
   "source": [
    "peaks = get_peaks(ppg, fs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 225
    },
    "id": "G8axC7u65O6E",
    "outputId": "347784b6-caea-449a-8279-43949a286c2d"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7fde79c44dc0>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,5))\n",
    "plt.plot(ppg)\n",
    "heights = ppg[peaks]\n",
    "plt.scatter(peaks, heights, marker='o', color='green')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "wqCdoirS6FH3"
   },
   "source": [
    "## Find Onsets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "id": "cfoeT9sJ3Fte"
   },
   "outputs": [],
   "source": [
    "def get_onsets(ppg, pks, fs):\n",
    "    ons = np.empty(0)\n",
    "    for i in range(len(pks) - 1):\n",
    "        start = pks[i]\n",
    "        stop = pks[i + 1]\n",
    "        ibi = ppg[start:stop]\n",
    "        aux_ons = np.argmin(ibi)\n",
    "        # aux_ons, = np.where(ibi == np.min(ibi))\n",
    "        ind_ons = aux_ons.astype(int)\n",
    "        ons = np.append(ons, ind_ons + start)   \n",
    "\n",
    "    ons = ons.astype(int)\n",
    "    return ons"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "id": "WCyxE_Sa6HNK"
   },
   "outputs": [],
   "source": [
    "peaks = get_peaks(ppg, fs)\n",
    "onsets = get_onsets(ppg, peaks, fs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 225
    },
    "id": "PbxWPuKf6OyC",
    "outputId": "bc1dea58-24b6-456e-d3ae-72b8d453c6c3"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7fde74bd9be0>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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AdsccFWJau3psFT/56p+pfr9++Zlb6gnI1T9xEw2YMvbaVMReeVQCPW2gV2wYSaBdjaIwVdIjdoGzkCiBXgLgXmPMfcaYTQC/BuCLZ/z9NwD41UsxuIiIiHBQUBEXte1DaFnX0ZWj6JmrAAA5nnQ+3wk4eeZkVV4wNM92P5+By6E843IYY0SEDwpmtZ5AtEEdZDb829wlnkB8LuqbK1s/vxzBy1M0CZ0vefaXV3/++2++e0s9ATkJFAmMsNiLknZpEo2htxtFZZafYhyN8nctNmMXuAYkJNA1AD7Gfn+w/KyBJEnmAbwKwG9e/NAiIiIuBlUgEhnvbQNlo+fzT8cofxEAWTb6xK0nMMpIvm/v11bK97U4unIUCYatn08DJ8Rg5nZkeQaN8eTph2FMuiPHGFEjEnYWRWFAzYTU5WBV1puMoXfH+mB9cHrtn1/G4JsVzcbFJY+29h6vB45xt77PdOk1sRfFa/ODXlRvbzN4iWw0yt+92BhHEsiHhARKWj6b9pa8FsDbp5WCJUlyW5Ik70qS5F2PPfaYdIwREREBqMvB4mS3XaCs8+HN78WRzR9ofD4Nq8dWcdun/DMAQIIerl+5fkvl+1qcuPUEFnoH6w8ErYqJEBvlL8bR9V/HoLhlx5Vn0BiPrv8mrtj89wB2RwnJbsTlYJ6+XeA+PheUqoHd6gl04tYTWOwdcT7bSUR6KMaBZA7f5GxssbKkCOhstZvf58IEKIEqEiiLMds2o24Rn+0qo/wIFxt5PTfF+2whIYEeBHAd+/1aAJ+Y8ndfjxmlYMaY240xLzbGvPjw4cPyUUa0IjSLsluzLxEu8jySQNsNvytLYkb15x14ydWfBgB4/pFPxkf/xUd3DAEEWJLqhz77/61+v3752Z0kFRFf8/mLAQCj/HnO5zsBfCxzxSe3ft6GOIduP4iw6xc3YphbW8K9Stg5JrLaFvGFQZoA/VTXHWynP/N2jvqx6vfrl3cWkR6K8aS+1xpSYcwUDZv51ipLOJEoHePxO4/jwmaC5fGXIDUrAHbP+xxUDlYeszDsYXNSOMTaTsFOnwNCQaTdqJ86703E7kIeME/tdkhIoL8FcHOSJDcmSTKAJXp+1/9LSZKsAPgsAL9zaYcY0YbQLMpuzr5EuMhZNop8ILYCuzUwCIHtyrJc/Z6ZFXE2mjZjOzVD8TnXv7r681u/9t2dmysivkxZ3pZATohtFyxpl7V/PgVxDn16QMTc1Rs/iSs3f7Tx+Szstjlqwnwr1C3ijUGWJkjTBGkCUfnD5fLMf96NX1j9+Z5v+8hlTwABLoGjKRPim5yt9pgJMTU+eeYkFvJXYP/k67E8+SLn88sdIUogOmZ+YNejnVbGf7nMASHgSqBJ9ATatZg4xHi8z4CABDLGTAB8G4C3APgAgF83xtydJMm3JEnyLeyvvg7AHxtjzm/NUCM46lKL5+P6C7+P1CyLsii2FfUarr1wB5YmrwGwe7IvES54YLZV2Y3dHBiEYPXYKn7i8+uuLNcsPkOcjSbyZ6eWZzjeFILMsiXE5pHABrUJRjuuPMOWuR1yPpOWuS1OPg/XXvhVZMUVcQ7dBkwj5rpIxd04R4W0Da+ONQZJYqv8e5nMCJWe+YOb/xIHN/+5Pe8OfObXGUlyjpkVX87YnOgJFiC8jCwEIRl2+95aD6fMHPA+v7xB10Nz3WndXxjYa7LVJXxa2L3DJq5e/zns37QdTHfiHBCCmgSKnkC7GYWJSiAfEiUQjDF/aIx5ljHmJmPMifKzNxlj3sT+zi8aY16/VQONcEHZkqXJ6wAAw+LZzuezjkswRIYVHBh/i/N5xNZiu8v38oA6fS1oczDMn4tR/nwAuycwCMVrbv6S6s+/9ZV/IM5GU8A43qGL04Yys7x6bBW3v/Z2LA32AQBW+ofFhNh2KTdWj63iP33Oj1e/S7yYaK5cnHwBMiyhb65xPp+Fna5I2cnjO3HrCcz35p3PJKRinTD5FCRmDsDlP0fRhrGXJuq5vSgMspIE6qeJaNNTPfP5Z2Mx/9zG5zsF60wVdX6XkEChrcP5391qo+Hc6Md44tYTGKa2GQI1HdhpSYJQXEw52FypBFrfYebQJ8+cRGYOoG+uxHL+j53PL3cQOTDspztWiR1x8QidS3czRCRQxM5DnS0ZAwAS9L3Ppx+XYmHGvxexFXg6yve2o/6VAoArN38URzb/Y+PzvQhenqFp30xqrfFloQSSPU+rx1bxumd/BQDgdc9+vZgA2k7lxq031CUkH/mO+8VlbmT+lGLO+7wdO12RstPHt3psFT/9hbdXv0vN00+eOYnULODI5g/iis1/53x+ueJiTGTzAshSpgQSvMtHV44iMYP6g7KEcqfFDVw9cXZ955BAF0Ouhmavt1MJxP1rpOTb6rFVvPZZXwYASM1oxzVDuBjQPdtQEAokyFsY2ndrpymBjq4cRYZ9rZ9f7qg6JkZj6F2NqARqIpJAlymo1MIkmwCAxMyLsignbj2B+eyg89luyb7sZNSmpkdx9frPIDUr8vK98Rqu2Pj3OLBplVvSLLZDAm3RwhZaorGbwYkfTanGTvcE4tnkkFbFa5uyzQE984c3vn9bSlb5uyEZI829RAIlRlbmRt9refylOLj5XfZ8O0iRQuObzz8dV6//LLLi8I4aHwB86XO+svqz1Dz96MpRpGYRADAqjjmfX66YeCayGs+3wlhjaADoZzIjVN/rLEF/R8YNO1EJdLHkaojKBtje7mD0PC6Nenj8/Kb4uJtWngMA+LybvmDHNUO4GHAlkPTdJC+a+bIcbKcpgU7cegLz6e7rvge4xtCFwY405Y64ePDQOnoCWUQS6DIFlVos9K2q59DoalEWZfXYKr73FT9Y/S7NvuzkMoHLAXX53hehb67DQv4ZzuczjzPAXPFiLOWvafx7s7AdSqB6Q1xC0Dp8t4OTJVLiA6jJn52WASS45QX6zYiUEKu6ihWfui0lq2OHBOoeI829w55VRhwaXStWpADA/snXYTH/nMbnTzfqOeq16Jtr0DdHnc93Avg8NhEGcSduPYG53gHns8t9jqLOjwvD0j9EaUCblixQP0tE13H12Cr+8+f+RPX70WW519l2gm+czyvm3q0E+TDO5S9BavYB0JG/ReA6vh3l4AQa45HlEU6d2xAft1aSdrstK895VakfIxERC4OdqQRaPbaKb/qUb6t+303KLW4MDUDkkxZx+SF2B2sikkCXMVaPreILb7ZdFb770/6teDJ+xbW3Vn+WZF92epnA5QDKOufJEwCArAwGJeV7VC/f9u/NQmgGUQPaEBOuX3nmjvN92W5wyammHIwWpQvjfEdmovgzFFKWICWBbMeu5tK0VcoN7sEkHePqsVV88lUvBAB896d9n1iRovl8u1GPw16D1MjK3LYTfDO7LnwGV4+t4l+/vC4D2w2bF1INVBtGJTmQVcbQidiI/rXPel3157/+hndt6fULXRt2YjnYyTMn0Tc34IrNf4eDm9/ufC4Bvz0aMmc7/S/oGTq8OMSTa3Il0Ho53+62rHwRoMKmvzZHxtA7TAkEAC+9+tOqP9//z7tLpy8X0Lsy6Nm4I5pD707Qfe5nSSSBSkQS6DIHPdTrijax2u5DVCbQK67F4Y1/h8SMdlyZwE6Hr5gxMPLyvd4+5zNpFnuyTaz365/3VdWf33Pb3Trfl9MfgzHZriIWecynKQfjwaJUCr6dRNpmIAmkVQKduPUEFvrbV7K6oSwHI1CgLy07aarmdlZJTVViXPnMze2o8QHuc6chWF929WdXf94NZSe1J5B+w+gogVK5Eep2lRfVa8MD6rWBf5fzGztjE3105SgyswKgbuBBn0sQmr2ebCMJlLP25ppng9a83bYhC7ln3OcLkDVf2G7w92urOs4+HSiMQZLY8lggkkC7FTRPzfWzXUc8hyKSQJc5aLOoCYh5lkJSr0wZqwPjb8J88RKMihc4n0d0gxQz+wZXAAD2DQ+Ky/d++FbWvWhZnsUuClMZgG5lEMgJSCl5QcTikc0TuHb9VwDsLH+UiwEPAC8oyFltWVK9WfoEYIZbTqSFdpuh73VBSLCsHlvFD39OXXqy1cqNcSCxQNdDY4T6s69hqrmlW7b0e2kJQpqj5vpU5iYrMd5OuB3q5PdK8x5eDqg9gfSlI0UBVwkk3PBsl9EwrQ0rkzfg+vXfRmIG4rWBEx/nNsZbNkYNTtx6AqNsP4C6gYeGXDWBil6uSN3Y4uc/L5+N0UC3uaJnb7d16gkxoPVJoJ2oBBoHKJwuB8U3qSMHmZ0XdyJBcDlcx50OesdG/WzXEc+hiCTQZQ4KhDVBLt+knhdsevyMVWb2t34eMRurx1bxhk/6egDA1zz/m8Sbq897Ru0F9P5vvVd8XF4YzPW3PqBYD+iGRQTiqDiGDIuA6TmfX87g75fKE2iiKyOjzdJVG/8FR9d/w55vKw2UQ5VAZaAvmWsIvGPXVis3eMCnGSMdd06hOPjSZ7+++vOff+07tpQACinhXT22ilff/HkAgH/5suM7igACPCVQIMG6G3AxSqCcGUP30rQqLevCdrUcpzVgofTN0nhTcUJL815uJVaPreLrXvDPAFgSSEtq54EdbbazHIwu+7xyc0XP3m7bkAWRQOQJVPp87UglkKMGVCSqdriVRF6qI3ukBNphnkCXy3Xc6aD3cm4QSSBCJIEuc9BCEUoCSSZyv0wgNQtbXiawW1lvCp41UnWuCjmn6HiSFwZzZVZpKye89YCNWZNYPND6+eUIHrQHl4MJriNtigbmxtbPLzVyFhhpMmWV15HiWvDNnNQAOBScIJCqlfhxmi5EnBTcyo20NaPdwP7N2zDMn2fPLSQIKVA6J7wW2zlXbwSqtvg93ol+W1rw7mCAbsNYFK4xtLSsY7uUQLQGFDgLAMjMIefzWSBD136W4NwO8QQCgE869GIAwKg3VJPaoQbPxTZ4AlbnKsI2V/Ts7bYNWcg9Ky4DJdBEGQNQomqUvwj7xl8HmJ2p+C5KJVCvnBd3WjlY3V34usovcSdex+1ESNxB7+VcVAJViCTQZQ7aLGqk8U5mSdgZ5PbX3o75/ggAsH94ZMvLGG77vdtw8vQpFet9ORBHFPRcGMsD1A2n7a3uPs9vAwk0DijRsF5HtT9Kiq0nFrcLfJOpMobOdWRaY1NUqqm2ikjjU4Wm/IRInLXNibpdLqAjPkPA3w0NaTepFE4aEqj+97fSV8Wa0R7Fcv5FODj+DufzLlQeToK5xvVvSbc8QzlWviP1cYyYFR63k9eT/GKMoU1dJmxbxO8sTyBKOiWw81lqRnIfvPI+r8wNdkyLeKAmf8sqPBX4lKlS2eRh5FEIJowEmhRGTLRWSiAF0b+T30tCYYBhaTIsfTcrYneg7/i3XeDzqORZpPXm8Ob3YmXypchw0Pl8pyAvgCxNKk+gnaYcPXnmJLLiMK7e+Bnsm/xT5/O9iHqP+BiMke8RC1YOtrHD7vHThUgCXebYCMiy8ywFL0GZhdVjq/icG22ZwDd+8rduaZnA8TuPo1h/No6u/y8sTP4RgG7W+3KRS26WwZhms+mU72mVQGU52FbWOOdONyzZeVaPreJnmD/K1fM37Tj/kVC45WCKjapS5eATaRlWtpRI05LH/nGFkQe2PIu91V1+NllgqykHC1ECceJiq9UUqbGkPans6PMubCq+F2UolydfiuvXfweJmdvSDKVDsAaWg0m+105fT2iDPz8MMYYO9QTaHlKBkk6DzHanOzQn96Yiwnllrrfl5LEGtGEe50ZMhBP4eqJRfDndQbe4tKhghquAfH3QKoF2+ntJyAuWgBN3B/ONoXeeEsghggX3jNabFHYtSnZgx0mgNMtnxtA7zfT66MpRpLCx3uLks53P9yKO33kcGxsLOLr+69g//kYAMmWUYww9KdRz8W5EJIEuc1ysJ9Bmrg+kpZnUUJw8cxI9cwQAMCye43w+DbQZGebPxSj/5HKcO08uSYuLigRiE1VoOdhWBoF8ItU8h1/67K+s/vyzr/nlXUEAAX45mL68CJC9Y6vHVvFTX1ATadcu3rilRFpRmCqTrW1JTRBnRXP9MaHg5FtI1zPN+Nwgeuvm0RO3nsCot1L+Zm+alCCkMUrmGpqTVyZvAFD7xW1VhpILDNYDy8Ek34vWE5geBsVNAHbWepJXqgG9Eoi/x/0srUqourBd5WCAndueeeBmAMD3feZ/EM9p48KgnyVYHPV3FAlE1ysvjDohQ+vJIEtVqsMiYN4NRUVKKokPIu2kc6Eb59kGJTvpvSQ4CThl4mO0DertUDjlYILx+R0x0x3YcRIojaHTBL3SGHqnKYFO3HoCc9k+ALZrJ7C1HVN3Ouwe0arKFvJ/5Hw+C1yxyH/fy4gk0GWOym9DkyEKDA5CVEchsFlsO9GlGDqfTwO9/Fdu/iiObP5Q4/OdgrpdtjyYm1yEEkgblIWA/9MaEogHEVut9thOUPC9OOypPYG0geMXPevLqz+/5Z/82ZYSabkx6KUJBr1UZ0Qb0i430Aw1BO7mVlEOVug72zjn2kJidvXYKv7Zi7+z/C1RmdHS9ZZsOGlOpq5HW900IA8knHlmVzKH1h0xb8NVGz+BrDjsfH6poS1xoWevUgIp138qB+ulciXQZuB7Egry59D4dE3yAr00xcIgU62xW41QLyugTrKszPdV6yQnj7Z6DqVzjZTrFz3H41xWQubGeScan+8UFMaoyZxqg1peQykRsZ3lcWNlcoZUfYSr5m/YcsV3kFdMWSLbL0mgnUYOrB5bxXe97Hur37e6Y+pOx9GVo0jKvWFakmL0+SwUHgm0E4nW7UYkgbYAf3jXQ7jhe/4Aj5/b2PJz0eKryYqGbMqAsPKHEJy49QQGqc1iJ8a+6F2s97SXf6fJJSmQVimBnI4n2nIwu0nYysnOkatvoRHy5YJJIAk0npiq5bP0fvGNzlYvaEVhkCYJhspNhZuRll0PV624PRsYQLeZpvkwxAgV2Prs/Kde9WkAgFE2pzKj3aiUQPKmAQXWAAAZ9m1phrK4yLULkM2htG4MimcBADJsHbkVUuLSVAJpu4NROZjcEyj02oeCXkvVHJob9LIEw166ozxV+Fg03weokyz75vo4uy5ve5+zDc9Wk3ZFSSwOynIa6fPheMwInsPLJc4LUgIVeiJtu8vj+Fwh/V583bn9Nb+05QRQyPWg2KaXlt3BdpgSCABeds1nAQAW+gtb3jF1p+PErScwypYAAAns+yKJO3KPaI0kUCSBtgRv/NMPAQA++vj5LT8XPdSqcrDALPu4ku5uvRT81htsW/QUcyLW2/qjLDqf7US5ZEinJNdzR6dUmNuGThNFYHaeP3u7iQSi67E06qmNoanbj3RjxjcUmtLOEJCCYNDTkUC5MZVJplgJpOxgeDEoAggnY0y1gVGph7ZRTUEm8oWy7r2a54Ulibe/9nZkqT3m8OjarS1JDOx4pC1lqjpiJjaRk5rlLVtPqMRlUNyExcmrAXSXuPjdwTTkpXGMoRNx1ntSbB+BCYQlTCZFgX6WYtTPdtSa4prP65Jo9MyvzCmVQMxjZsvLwcruSgP1PM9IBcHc65cXwSQ7Ms4rjN6Pke4XXUeJce3xO4/jwqbB8vhLkJp9ALa2PC5UNUvQEqBa2K6Ya1iY3Fr5D4m8YogEqsrBdpYSCKjnDYOdN7btxuqxVXzzi769+l2qjAr1LtvNiCTQFoA2wk9dkC/YoZLOEBIoZNMD1Av7drCn1y5ZH4aXX/tKEeu9emwV//VVb6p+36lyyXGIEijQaLgwBvPbwHgHk0Db6DGxnaD7tTjqYU3RBW6cF1VnEOn94hsd6UYwdK6ZlCSQNsueF3qviBAlUPgcan/ODzLxNZwEqiI2AzKpoaBnI1eSQJtKhdPqsVVctWQ93H7glT+8tSWJge2yJ8quNkRujXr2uT0y2jrj+rqDznEcHH9rZeQ9q8Sl7g6mN4amDQ8A9NJUnPUuAhVzoQhJmExyW7KqmaO2o5yG3x/tRpiu+/JcX10ODpASaItVoqZOEAAaT6Cw95Jw/fItOzLO436M2sRHlllFlaRpy8kzJzGfvxz7J1+P5ck/dj7fCmjvl4+t9uk6eeYkhsVzcWj8nZVhMH0+C1QONtih3cEAXWfgvQBSOgMQK6PotsZysBqRBNoCUGB2SlgOdjGSzooEUipECCGlDNvBntL3krQpJrzm5i+t/nzvt9+34wIDgJNA+mBOe9wkLzDq6zJzIXC7zelJRWB3KYEqEmioUwKN86IqB5MGIU5mTnDMxcw1PNBXlYMZvTReO0dd1BzK/Cykm+kQWTywvYbX9BwaA1UXjBDDa8qgaoxrQ1AEqlhDyLfVY6t4xdFXAAC+7zP/05atJ1TK0jNXAABSs+B83obaEyikRTyQOi3iZc9GKAEXisqDUOn91M9SDHsyQne7ymlcJZC2HIzmpxRjhVcJvSsLg96WqykneWA5GFMCSZ9h/h6+4xvesyPjvLwwVVmXuATauEogibLXeqOU/plm0fl8KzAJTCATttpK4ujK0Woe7Zurnc9noSgM0tSWxwKuAfZOwU7yONsJWA9Yg3zvst2UfA5FJIG2ANRmUMp6czl4r7AZVamkkxb6rTZq5X93O9jTigRSKCn4AqNp9byd4K1ipRv9cCUQkKWpWFocCh6XhijLgN1FAtE7uW9+gKcuTMQb8EluMFdl9gM8FQTH1F2PoO56lDPJv474KNQmmVr/EfpeiZlDr7gGgGIOLWx72JHie9F1H/V1qqiLldNrkAeWTtUkkHx8CcjEd4tL99irFHrdtQQmAJUXixZ+iUtiFjpLXGpPIN18AZT+LVV3sCRoHdqWcrDy+dOpZgv0sgSjfiraJNC8kZkDlQJrK8ppLsYYmi77sJdhnMvbGk+2WQmUJqiUQJoukFriiH//nRo3FEbvO0LvV5rKzbxP3HoCo3Rf+ZuuC2QIQn3ByHB5O/xEh5l9jw3ssyHyijGWfCMz+p1YDlaXdz/NA9khWA9Qo1bG0NETqEIkgbYA1H5VmsE6eeYkYBJctfETuHLjx93PO0ALxzg3TpA2C042VbNpV7bzvBiEKJx4sLjVi00o+OIiDW5DSaC8YCaZWyjfD32e+OYjZELfqaCvdWhxgM28EBOSuTGY65McWU4cESQLGs0pS/lrcNXGT2CYf5Lz+SwUxiBNE5tlV8wBhanLwcSbA2XGkcZ/xea/xzUbPwuY1Pl8FnKmcJKTb/bvLQ57mBTyuZdn8re6pIYH7Jr5sCKBFOOrux5uvS8VQbOxnSgNaAn07mpKu7VodNBZuLGzxGXCSMgk0flmFY4xtNwTaNtJoJyUQIqS2qIsB+vLlEA0P1y7/ku4dv2XGp9fKmxOCiyPLGGnVcvRezzspTAG8jgvwBMovFS4QC9L1eU04zy8bArQqcS2E9wYWnrtuSdQv5eIYoDVY6t41TNeBwBIMdhyG4TcGDXRB9TEhaTZwMVg9dgqvuK5bwBgDYPFXjGFjW0ogS/tmBiC0HeMlECbk0I8B+xm8BhUej0mbE4EoicQEEmgLQHtiaWdkqykcwAAyLBcbWC6JIzGGCfjIN2YhRo8UlChYU8v1utIs6ngAYFm07OdLTadACaABFIZSlM3p166xS3iw7JDTlexHRrMhYAUGIeXbGe7J89vyo5jEnJxEK2U09OcMpe/BACQorv0hI+vlqrr7rO6U4pSxULjHxXPAwD0Sim45HtVXc8U5FZoq1FeLrkdZq0EjZ9ATfbrEwSS+fpi5lvXl0ZXAh1SGksKoKe2UAkEuCUuP/mqn+vctNDc2ctSjHo6pUdebngA6wkkVgIFXvtQhBCL1KVq1LNKoC7VTGN+MEn75xeJvDBYme8DCPcEInN9OWlnf1qvM9l7GW5LAKQBxtCFMVWHOzEJFOhBuK1xnmHkllJp10tTsRIIAK5duhkA8Nqbv3LLu0blhak30ArlFn237UjOftLhFwIAXnHdZyq8Ymxs06taxG8NaXox7xhPJsbSMLdrsjpui0qgCpEE2gLQAyldoGxnq33V7ynmVe3uyEdEmrnlGWKNAdpESQLRhPfg6bMwJlNNeHSu9XF3IEfg45KW4t1x1x247Xe/GQ892YMx6Za32MxNvYhKM4J0LYY9mcSdn6tX1ulvqTF04PPkkEC7aDLOy2twaKEkgdZkJFBRWD+LNFF45yiVQFR6kmBYfpKK5eN5gcr3QdsdTFvmFvq9CJmRtyoP6XpG7+S+OUveS0mCiUPabV/XM2lZbVEYx/utEG44aZPZtdms14RzMKannm9D1ShFYTCvNF0HagXQdpLUEqJ/Um0YEwz7qWp8hbEbHqDsDibMetOzkKXJtvhZ0a3WJj6yNMWwn8GYbkVloxQPoy0pp5kUtem/lkAj0mOo7jZl/96ol4mOqUrjikM4sPktgMlUJbW9VE8CTfKCkSX6pJg00VrNO09egDHJlsd5BUvoaBMfaQoMFMQu/b3tmKNC/P34EhLi46IFjUtKlgJNY2iND+HHn1yHMTJCh1t/7Bt/HWDk5adr3O4imkR7SQkdgay1J9jNiCTQFoBKW6Qk0OqxVfzIP/ov1e/XLck6kdADTYuodIINNYaulEDCIITaNV67/ks4vPn/ANB7HRVGk/nSZ76P33kcZuP5uGrjJ7A8+SLVGEOQFwZLpSxcrAQig8dhT519SZVlLiFwPIGU5ABhdymB7E+taWtuaqWNlEzjnX02Be9/1fUos1npK+bkLb1tORgwVPrgFEVd5ibewCgNgP2SmisXrhN/L7rumo5CNNccWR4BAB47K2sCsLmdxtDsGkrVB3R/aI6S3i/abHd9p3pN+B+4YtPOsZr59mLmmpC2sJS53k4DSck6Thv8sG59dYv4XpaKs97b2XKcz38atYf9brVqputa+vPG0aVnbkk5DVdQaK8dxV2jnq5UhZe5StYTKoE7MP5mLOWvwah4ofP5LFDnSG13sMLU8WtIZ0bps3H8zuNY38xw7cbPV12jtjTOKxNwvTTRdwdLEwwUXl3SufdSYJIbdeWBQ9xvQ9k/XTcVMV64LeIle47jdx7HxsYirtl4M1YmXwWg+5mqytY3fhArky9FZg47n88Cv79b3YDhcgBPUEn3vrk3l0qJ592MSAJtASol0KZ8wnv1TV9U/flPv/ptsk1Z+c/XGSa9Eihk0y5dbE6eOYkEdpM0X7zc+bwLIUSVQwIJJ8mTZ04iM8sAgJ65VjXGEFgSSCcLJ2XJXF8efNOl6JUeLrMIgouVSfPNpsZQj9/jnUgChV6XUMlpXqCqSxd70wQoI1aPreLTr7fv4w++8kfFG56qHCxECRRokgnINxT8e/zMF7xZ970yq6jQZm2PLFtFlbQTJJF2o77cpyvcp4ORQEJSvCKBhro1hYiErmfw5JmTVQniXPFi53MJ+Lul8gQqDIYlEakrI7Pn2M6MoWQudJRAynKwwtS+hf3Ueo9I1LYThwTaPu8nzdpQEbp9ObHwVZ/0VdWf/+/X/vWWlNNMCoOFod7EG2DG0MpS4bwo/WWEHeDqEjj7cKRl7CYqqQ1QUtgxMlJB+r1yPQlk4zxrGLyYf67z+aWGMQbG1Gu5OKHDSSBNk4IAI/9QFMbUirSAtVwzxtB1j8alVkemCXopEa0y0jTDEgBgMb/V+Xwa6F1KYTu59c11zuezwNfzs+s7kwTazpLLSQC5WHjCiagEiiTQliBkEuLGuGJyIFAJpM2yV8cpy8GOrhytAn7/8y4UAZvbEO8c7seUsNeha4wXQxBQDbw0gKmUJYNMUa5SZ4pnBRRVjfLpB5CYhSCZdBGwYefHLQxkJp7bCX5dsuIKPHD6pLrleIgPDmWxw5RA8mtIxrDSsknAfi+tsqwobEB8MeafIQu1Wj1QklvauWb/vJ07pEEZZfEXBj1RQFw/hx9DWhxSvZ9OOZi09LQc32KpBNoQZMrImw7oDvKPrhxFava3fi4BvVtaIoKXq2g6JVKgudUbLLfjUff4uGrAKoH0Gx6g7mQqyXwX1bXvbfl87fi+aH3wymsCyO4bf+e3ytjcaRmuLQdj5eCAbj1JU3uPx4JjqDTOJLZ8OTFz4tK4hhJIMY9qy6ZCng077yyXv8njvBDQq2QJOJnBM8DKLZW+exQrbEcMlQfMo85+Q3gMrXsfO30KxkC17tH1UMUAFWkn7w52dOUoEjMHAEjMyPl8Gqp3DLZ8PIX8HePr+ePCpNN2or5nj6v9jkIQ0v2U5lKtaftuRiSBtgAUSGsmIU7gaLtGaZVAk8K25UwS3aY9rwJiWZvSE7eewHzZrpEgDyrqcYUYXksDihO3nsAgXSp/y0RjvCgDRRPSDcP+Pc3Ghy5f2hFQUI3y8uTLcd36ryE1+9Qy6SKQVKT7tTDsbUutuAZ0XYbFMVyz8WYsT16naqUOMJm7YoGizG2QJ1CA+bfGqLEoqGxKTkbWhBhlwQMk5AHPhmbuDel6Vm2IA0r+AHnbZnoO902+Ftdu/AL6xY3i5zCkbKJ+duVrisan58StJ7CQXeF8pvFgoes+ClACpUmCoeLdIgIT0AWLIQkCrfKlVgKltjxTsQGkeQaw5WCArMSIphetCisEVQlUP1X5xZHfUVUOJrguTvt2RScyDfLCoE+dOpXzdGUMXW5cpOXxk9ygl6bo9xLR2kClcQt9u6E9NDoqLxUuvBbxCoJAazQcohI7cesJzGW29MZgHcDWtVKvCVqoyBxaGzKlgijEyD8URJZo5lHHwFc4Tx2/8zgubBpct/6/sG/y1QDk5XtVV0GtoXzCSXEZaTqXUUKjjNE7nil6x9LE/v3Dc9eI37FJYXBo0SqPpeXnwPapc47feRyTjWtw3fqvYT7/dABbXHKZ6+eBqhwsGkNXiCTQFoA69mgmIf4Qa7tG0SIqfRGog0ZIWUeZQBS3r/zeT//B6ndN+0o+B2ulfoB847h6bBWveeaXAABSI2uxyc3dhrntRqQhCELKhAC7cdQqgXrp7BbxJF0lOWu/uM75XDa+OnjRGEMXnATaYeVg9P375sry5w3O57PQ6Bwl9kcwSBJdAMi7g2neZXoeNAaDPNurzrwM9BliQpASSGsmm+jKwarSmEB/BKmqj563UX4MAJCZg87ns+AqgZTkliJTpuk2uXpsFf/8pf+6+l3b0pi/W1pfql4WZv4NyNeg0ASBWxqrUwJdTHewKvMt2PSEJCMutjvo4rCvaolM322keC/587CVSqAsTWeuxdPgdweTrg1FGa8NFOvJ6rFVfPGz/zEA4Ns/9V+rSmp7aYphpid0tPFQCLm9emwVX//Cby5/K7a0lXpt8JyIVVhA+ewmQKIsua6UQNtRDlaSJRpfwBDFxskzJ6tSqcXJ5zufd2FzYs8nmUerMZaxTU+hBFo9topveOG3AQAMcvEztXpsFQfm9wEAfuiVPyJf+4zBFWXH2ceFHWcvJmGtxckzJzEobKe6Uf5C5/OtwMUogWKL+BqRBNoChCiBQgIR/4GWvwioJZ0Ko2FjoDbXfMV1da2spn0lZ+Kl59JsRjiese85AIBXP/N1ojHSpHbVxk/gys0faXw+CwVTAsk3jhR89xRlOPYnle9Mu4YkXTWwY+mZK5zPRecyFLD3VMoS7jGhWbC3A1XtttkHADDYdD6fBfpe6nIwRrKIn/lAo2FSXqmVQMryE15CAuhJoExhrMmh8hEp6q5n+mAizI9tTvgu189bubEo5eeS55DMSQH5mjJprCndx2mVgC+/5rOqP2tbGoeaE0+K2nRd6/sEyOdqa3y9jqvWfxL7xl8LQJYg4Jt0iSqS3vuQ7mDGoErm0POhUgIJr/3FtRynNUW3VhYFApRA+gScFpOyjGYYoKKi664tB6ONbT9LURiIiTR6Fp66IOt4CJRKO2U5GJ8LAUX8yj2BFL6bL7ryZQCAKxaObGkr9WrtUiZ0uGG7yhMo15MeoZgUhbqFfYiq9+jKUfRK02Ra++jzLlDstJkXIm8fgBLcCfqpjmh9wZGXAgCuW7lW9Uxp12WgNpdPE/k8Zc2r53Hdhd9Av7jBnnOL1DnWWoMohcL5fCsQogj0LVSkBO1uRiSBLjGMMU57cylCAs66JKGnOl9eFFXdvLzdqLtga7MUWvCYNMTwWhNokQRc43Wk+ZzDZr5017AiFRRKIJrsulrE1y1y7f8PaZFLl/3w0lCcobDH7VwlEF2XzKwAABJk8tptY5AkwLCn9zqgoJ2yWV2g90vbIYiut6bLBI1vqAhQad7Qlp9ypY12jgJCysF02U0iWbUlf/x7Seb5+v0sSSCFh0Be6EtPfRWbXgkk8WCp/46krNgZX/nX5/qZylulLvlTEKwB64k1vl7GwNyIlcmXO5/PPJdS2p4XBZIErIxRp+jl3cHs+QVKIO7HJFiTSTE7l38qrrvwm0jNglwx65HHmk6a2UUogTSNDTSgDb4lmnVrXa0E0pWD2WuRViUu2vIijflsYdwW8ZJz+YSzpLsl4HkCKeYAGlOhnHO0cMu65J5AREQAdr1UewJtixKIWtiHkkCyY07cegLDzPUTla57fGMvLSWlBFeaJkgTeQc+WoOkBCshhAQiJfb8oCc+7uSZk5gvXo4UIyxNXuV8fqlhrTWsUsmA4pytKbkEwp6rRtOWqASKJNClRohUFaizPUCAtE1pcuVuNpWbF+WmIrTmMi+KqtNEiCeQZoNQt9iU15fbjVkJk4omOzJQpdIYzcSVJFDJ/klJ1WXkSzXKg8wG2weHV6pl0nW77CFOKWqVa0+rbMeRQHRdlgeHAACL/YPyluNMdQDosthdHk4+uK+S5l2jZ/6cohwsL31VaHySDbyjSNP4sSi9cwCvrbQiQ1yVgwV4HalLccsSjZGQcKLncNizHQUPjq5WPYehXdkqJZBgs++Q74K/z6+VNnvNnwttUwPapAZ5WWiaIZjF1s9ngZdjSa4hKUsA6I2hi3qzWZeDSd5lUirKiFnaaOwbfzVSDDEonul8PgvV2jCkphfy+ZCIakB2Lfl3CU1adY+rqFRboS3iqbudWFmS20YDdI+1pMJTChKIlEBZ+Z9ICeQ3UBCT/WyTH0QCiQ8JAt2vlPz9VPdLrwSivyf16rwY0N5BNY8GeEauHlvF177gG8vfUlX5nrMHU5LHgCXGJeWxQP3MakkguibaNvZZmmBukIm9y+y6Y7+X2WJ1zuqxVXzF814PAEi2uOQSCFOY0asYPYFqRBLoEmOS6ycgIGxhqwJ2tRJI34bSX7ClL91mYGYtL/RZ9lAlUNX1TFE3f/trb69+v37pOaLJjoanLY2hUoalUQ+n1zZVm++6Rfz0c60eW8VzD1tvo+94qdwHoDpXOZ6DC0M8tT5x7sMsuMbQO28yXj22ii97rl3UPuvo58v9EYxeGk/HZSkwyBKVjwCg75ZEAbGqHIy1ATZGljV3FGkKtRLNo5rNPn9/dZ1BeNczXYlmL0vVHkkZqTeEBMjqsVW86GrbTv27XvZvFD4d1uiynyXy7+UpMCTHaRUznPjRKNEApkgNNIbWeQKV6shE12hgPjvofCZJEOiVQPWmRasC5N3BNC2Rq5I6IZlblxrbe6wpNa6Vdrq1smDvFyBUprHncas2BSHvfXUsmaH3lC3ijZuM0JMKemNdQK5i8RsoBHkCKWJsekek8UkoeCnzoJeqSpIcEkhJ2knX5IsBJYI0TRSITNeW8L7oypcAAA7MHVSVWoXspTgxPshSjIVKbHqXpeo8At0nVTmYISVQJj6Oq3OwDeqc5x56AQDg21/6HVtacgn45WA6QlJbWrubEUmgSwzOIOtIoPrPWiXQgtoTqFAFc/xcI+WCHVpzmReFWnUU6glUkUCKY77yuW+o/vy2r/sbYYbC/vtaIo0m/2cdWcL5zRwfP31BfC4KAruuB41lTUEKEEIzenWLeKtikQZn29XtAKifiZAuWqRkU3k4lPdL3iK+DrBUndkqH4EwA2VAdp+5Ik1TtpKzZypEdajOsJU+IuPciJ5D+up0nOZdThMd4QSEkXZ5UdQbz1AlkOA43sJaUtJxMR4sVTlYqDG0JjNv6vlJ02jgX7z0e6vfpdlQhwQSEm9E4GiJBV520leU71DZ5EDYbapZaqxpOU5rpX4OTZMEo75cbcu/y1aVB1SeQMr3Hqif+brUSrZOFqU6R1sOVpcXaUjWonoe+5lMCcQ3ZBqilb8rGuUW/d18G9QyADOGVtwvIoE0htJjR7G4tYrqgiuBlPOoLSPd+vI9frnF/qqOEigRdQcD6vlCSvQR6N5qPYGyNMFcPxOvmzaR+RXlb2bL1TkbE/3cEYq8MAFqe3sNE4rBYjlYJIEuNWiBWhhkqrbXjtO5ktW8GCWQuNMEZeYUwRXgBlWaiZJM0Oy5dFnsJNEthiEkEFeuiDvvBEoRSSZ8aHEAADi91m3YSOeqPVxmj5GY9LWAsixuGAro/VEWhnJjSDIbPXn6ERiDLe12ANSByDnV5rv2AwBkG4tKQp4qJeSsZbZmA0NBjso7pyj9ABTkVvUcJlovoUIfbAZmiHmWHpDdr7y6X1CRLK65tvx+0Zqgeg4Nf/+3ngSSZnp51k7bjYkTzpo5niuBQggxTanFS676zOrP933H/aKAe+xkr2XXnDYttrRQ+R4TCZTKu+HkhU4J5JcyHlCUGocayhelmpLaqUtiom1RAuV1dzDtOar260TmKIj0HieBhOqGzao8XkOy2rkQAAbC+ZAUIlpTfsc/U7Hm0XWXXP877roDN/zI5yP99z11oomvef1MRpgCtdIOCFMCAVu/+Z7kptNn0gdvoqAZH/37Ws7OrQiQ74loTuylcuKO5g4teVx1kBaWddkx2ufDloPJ5/vnHLTqnH/+sn+x5eqcjYrc0ieTtciNqQQQUiUQf8eGyu7YuxWRBLrEIKJjcdTDODeK1qYsCFSwmkCAJ1DABos2jVVQFrBAnVc74Wu7RpRj7Cszo4U+6OHBpXTCqw0DdRL+ypBX1ba5VAIJN5y0WIZ0R6HnkMgtTQtbgPk+CBa243cex4VNg6Prv4GViS3V2qpuB0D9XXQZm6LKNkiD/urZUCv0CvVGH2AdRQLkyANFqQV/5nWETr05CDGgVBtDcw8niQ8Ou1+ats95gUpNpZlvKGjUPIe825y2hX3VsUdYmgTUQX4XWcLHog0WQ7uD1ca1Cl81prLTlFrw+S+ky59kHiSPGQDqjlOWQLJ/ro2hhSSQsox89dgqXnjliwAA3/qp36kqZQRYwiSXP79ZmmDUkyer+P3aOk8gq0QL6g5GHo69spObUKVAPj1qTyDK5itLamtlmnLN05YKs3let+YV1fGzEpJ33HUH/tlv/zDw5Hdg3+SfqhNNfM3TdAfjZZqDTK5KnTBCeKtJIL5WhpDpk0K+JwpWAgUaBtOc2M8SccKa5iXpvSIElYMV+nIwPsbt8N0MUc6Hwt0jKjwZK7I6kkBAJIEuOchgcTFAnUPQKoGqThjiNnkBLZH9kp+AEg1t5nZOYU5qx2h/zg91GQcKKlRKIHatxeZzzDAwzEdELn0svACry8iXmPSQybuonkOddL9SzdG7IvheJ8+cxKC4HgAwn3+68/lWYCNgUfNr+zVqijRN0NeSJcrnCajJap15PfkB6NsApxW5Jffc0bb0vnglUPl+CTac3PdhqFBi1F2qdGboIYq0SXkNNYRzrcDQK4Eqb4+OZzfU04OPj8oEpeqcSW7UpKKvRpH74LE1T7h20f0dZLJ2774n0Po4F18LxxOoMoaWq480CmKgXiu1Gx6AlU4rYqI0SVRKoFBSQVOWXI1LOU8D1G0yqUgWqc+iX5a8teVgdl4D5GR/wefQQMWn5jl0Y9Hpxx2/83gVS49yS2BqEk3+mhfaIh6QvZcFV81v8Uafr5XadVnr/UR/T+u3k5fNFwDdvOGWg+mUQIDOBoGuSUicMteXdwcD6uuo6fYXClojzysajoTaO+Qhe0RPbbdVpP/lhEgCXWLUSiArgdYYkxGkSiDaRPcyZaAfkNHzWyKHmLVq1DnOwqbsGqE1yaVgX0cC6RVObtCj6fRV+wkAurbN3Mh31sJG311DChBCCcJKCaSQdB5dOYrM2I5dRXLW+XwWQhea0HIwkhZLg1vav6VJgqEye0glWqqsKAUhSsUMzRuA7D77JpnywFFvJh3SLQKw5DE38pbMU7n3LouNCQOCaKCe63WeQLUX01aWg1VlncK1gatO1OVg5D+k9R9jm3ApEVmXq+qUtm55hu5ci6Oe6FmisgzAvvuFcZ//WSBSAQD6qUIJxLqKTgp55rsqEVB2IgRYJ03FBkvbHYyrsKXzLpUlf+z0kzDGdKpFeHc6tSeQR+aIjYYLd75Wk0BKXzX+PEresSpGUapf+bOqmUM3hWq7k2dOIoEtvU8xcj6XgK8NfYXJMJWsArqS64ljnSB/foM23qyJQviaomsqoyH76XwLVcWC3hhaU44f0lnQ8Y0NaGAxr+ymS/dJE8OGokqaChW+9Tx6ColZUanucrYvku6Zc0ZWa7pj72ZEEugSg2R+S5W6QSdx7aWJmCzhJQmjvnxi4BlibZvHOWXpGf/3dZ4l9UQuDUbqNvZhSiBNffnFlYPprn2j44lik+qUucw4X0i5CR8fwKX7umd+QaGaO3HrCYyylfI3e74us1FaaB448wAMugN2Dt5+VRp8c2mxNLj1JeSagLiXpqqNvj3O/t31sdyQu/IDUJCR/jOvKVnVtpV3fNVUfha2s42GZHVJIL0x9LAXJo3XlNTWJVB6Ik1jXs/btttjZp+Lf2d1dzDjqyIVRHoW5jGl7VDFPVs0zztgFcSSjUFeGGSZqxrQfC/KhpISSDK3FR6pID1fpQRSbniA8E6a/SxFliZik22CVGVz/M7j2Nicw3Xr/xPLky8DMF0tYoxxyF9td7CiVG5Xqi1leZHeGLosFdYqgbifjVL9qimTo/lGakBNkHrnHF05igTUUSlxPpegMoZOrKpX7rtpqnusSbQUPFYW+h2FxkMVIam0MwCAub7O3kGq3GqcL6iKgJlyp/KObs7+RpwkCEuCENFqy8Hk6ybN09tRoqVVzh+/8zjWxmu4cuMncO36LwCQq+5C5lSn5FIRC+xmRBLoEmPCgjlAU6JVB9IaVhMI2WDVmW9x4Mj8EQDNhBeoBDImuEW87UKg2wQCuowD3xxpyBwATBYuJ7d4VyaRFwsjFbvKd/LCVEqUEBKo6hwX0MkFqAN9CQm0emwVr3/e1wIAEtMTdTughWaUvxALk1cCkC80zoKteJez0IA4sd4P4o4ipfRZk2EuCoPC6EiP+ly6DXiwEogRGNpuc4AuQ9wwhlaQW9SJSO5bRsa1uneFngdNdp5KNFQklSfdl8yjtXeOLMjnnibaTFztqaQL8inTG6I40HruhXTroXMdXhri9NpmJznIu4NpW4DzspN+VQ7WPd/wFvGA/HrQvK7ymPETC8qECQDx5iAPmDdOnjmJrDgMAJjPP835fNq/31MqgPnxSYKKzJESVeQVUx0nNoYOUwJViY+ezFiXJzE1pcJE2s0pmyG4LaWnn8tJNBn7pTRttSmGJD8meZMHVHFDTdzJ3sv5obwcjOKh5fGX48r1NwKmJ46HalW6vuNkpahUlshqjgHsc6VVRvnEuEQZCfgEckiSQLeeU8JfE6fTNdiOcjCaP6XKaJov++ZKJOg3Pp8FihFHiiYR/D7bZKuu1HA3IpJAlxhV4DiUl7gAbrtsrRIorV4E+XFqh3/Pf0gqs5wEBMRA6QmkDL5rJZCuHCwPmMj55KHNiOhL8WzWN3zzPTuQ5ottiKs/Xb6R2qDcDQ6k78pzD30yAOAl175E1O2AFpQjm/8Bh8b/qvH5LPBrJq3fpta8gJwE4qWCg0xXvqN9nkj1tVSWrEolyX55gaocLNGrMNSlTLwVrYYEapBbAvVLYGknEYT1Rlp27ekd1ZAmlcKprzCuNrRhTcWecQ0lUMfzFGLcSTDGboiHyuvnkIpaT6Chtoyh/vfFZYLluY4sD1EY4Mm1zdl/3yFydCSQMXBKHwBZxyma1/TlRSWBqXl2PW8qzRzgkECKOcqOVXaeoytHkWK+9XMfVdlTpiNkCVUZbkA5mFUC6xRElUpUpQSqW8SL1a9EjmXa8iL79+YGSvWrQwJNP2712Cq+5vnfVP2ubatNl5nmeY0SiMcNgHyN1TRRobhn/+RrMTTPRM8cdj6XjFFTVuuXg4n3DixZoI3n57SGweza97JURIrTcYSQxLh0feBjnB/IW8TzcWlVtyHQtohvzJem1/55C6oYURPfFFEJ5COSQJcYNOGREki6waoIjKGG1bQ/qbRLXg4WXter9QSaBAb8IZtbmpDnArqlEEJMaLWkh9astVIC9eQki28MDUz/bvweaQzd/HNpPYEaSiDhc09S01zYJWXagiJZaJzudkKJK204AblChxOE/Z6yrWya6jr9lZuy5ZFujqKadA1ZwiX/WhVGaCmTunMUkxUDujI3UtpJs+YFC1yk5wLqe6ad14jMkaup7E9NmVvDE6jjXOPciP2DfFSqrYDrp13z6u9Fqt6ADLZ47bI/Dy/aMpTHz80mgXh3MHU5GDNOpY27pONU7pER8utIGwNFEij31hSFUb6WgA9Z/0/cegJz6RXOZ9PUIo4SqB/QIr4kjrerHIye+7yY3UXLPZd73SWl9X7Junb90iqBNK3Dn3/4xQCAKxevUrfVrhNw1B1MqiopHAUxICNM3FbZ3X/fj3sys7/182nnCknoAHU5mLiddx6+d1Argbh6W9EdjMfOck8gXrGgS4xTOdikMGqDbY0nWyiIGJTO9SduPYH5fk2mpxiKVXe1J5BOlVYlCbJUlPzY7Ygk0CUGbQ72zdksu9SMK6SUibcBVymBygxxyESuzsxdBAnUU2xEACZV7esyRNxLRBvYao7hGyxtaYyz8ZF0PMndUkFg+sRMC95cP8OTa5uqVpdAC0GoMmvlHWBk95mksNJgwl9oYOTybutNlTnnlRxTBcTCDbhTKliSJZLSRJLhq0hF8i0jEkihcOJkpEjhxCX/Ic+8IuNI75dGHuycS1Gi5ftZaMvcNP5expi6REM5h6YpbJCkVKRQyZpGSTEvJEvywmB+qPOH4OPLmGpLo+qh9US+5tm/p91QhHTErEzyhckjvzuYf17psVU5mLJFvOZ89F6GKIHU5WDGlftLNmb0/PazRLyRWz22in9y7OvL38xMtUjdpCGtNiw6o1sgSRJViRBQd8TTkkBFQMxG8RqgIN94oiqga9/coKdUAskVGPS9tZ2p+PjShIyh5QRm3SLe/uwqVyG/Kc18SvGQgd2bZNiv2Hi7CR1RjBLQcRJw43KNEbJDAokVIjWJ2Ut1jTkI4nLh8u8tjXoqtR11FiWVkzRuqzyBtkEJRHOp9FqsHlvFz3zh7dXv1y3dJFbdkQ/ZqC8XQJDqG4Aq2bqbEUmgSwxaQJdLEujs+lh0HDdClEsY7U/aLGmMoXtpqjKgbRqGBpBAyolc7XVk9McAYWqlkDI3TtrZYFA4ceV1Rl86Rr/0bNZxFFRetTJCXhicviB7Zgl+OZjKxDNNKi8hKalDE7f0eV89torbX1svNNevyBeavDBYVCpmiCwBIC5ncpRAJPsXBKA8MycOQrxyMM0CqjWG9T2BtBkbVYa4/F4Lys1B7Rejb4tOkn/xNSzC1VTSv18dx8gtubqU5qiyHEzhqSIN8t1uNrrspDF1xzPJufg56/bmss5W9NhpW8SHKIFyf6PU8TxNCrcbE6Ak31npg/33ZPc5S0KUJSWBqSh9uJikE5f7SxQpNM+OlMqS519h24e/9NqXzlSLtPrzKc5jjClVJQFKIHa/NF5C8wplCVCXngFEAilUosr1i5f9a73fCF2qY7rGIUoBpzuYYqPpm9YC3c9J9Z4o4nKKhxLYv3t4dK04HipMnXQqjCxGqTyclHN+CBkJ2LlUqyCk7wXY8kQp0RqiIqRna3HYU/mQ0vxL7+baWEbqcJ8eaSOKUNC/r/le//iWr6j+/Gdf83ax6o43zNF2xgb0HXV3K0QkUJIkr0qS5INJktybJMn3TPk7r0yS5O+TJLk7SZK/uLTDvHxAL8G+edtiUmrGVfmjDBTthlnWVtMdrChg20oH+CNozZpDO/Zws1Yxm59zEkhnakjYyjI37uGk29y6G8eQ1tzA9AWRgvQjy7Yd6uPnNkTjIoSWg9GETOSROOCk7iUKUpEvLH/7jXfLFxpjqtJOqRKoUXcc4J0DyIJ96hKkUQ/RuUgJpJo3uHeO4L3kz3yIbDcsQ6zLttcKwvD3S7rBoo2ZpvRsfBGyeKs+0JD99ic9hxolhZTYyYsCgyxVd/ehc6W8k5uGmFUSmBNPCRTS/UVjXA2AeVl0b/5CPYEKUxvQEpEUogSSPIvGmCpJIC33Beq4Qa0u5WSEcO7g65fmeaRMfJLM/ntu8kdXxgjUBEuSJOilcrUSn0MBOaGh9Zipz2X/PBSqXzk5pimb4glJ6bWg44hI63ov6d+9MM5Vqi2gfnZJMaMpw6sVxLI5h861MNQR1avHVjHo2Xjvhz77h8Xx0CS3CWRt9zKgLhfWkn2ALtYrCoNRP0OSKBrzsMRdP0tFpLg/Ru36sFgm4bSJjHmlMv1iPT814K+wuHEA+x4aw2ueRJYnMuGsm5r5Y7eikwRKkiQD8FMAXg3guQDekCTJc72/sw/ATwP4ImPM8wB8+aUf6uUBWqD2zyuVQBRIDzVKIC471XQhKMmScrHWbBxHilIQPkbNMc4YNfJ9oyeOgDCH/zAfIfuTNoHaUoZEkTFzOsdlszftpGpaKdVr2g5hjU4uYuO/utsBIF/oScmiMcfjkL6TACmByusiLe00blZUtlmyP1Onm4s8O0/dkqTdRAC9bxkF+qqNdO5mwTVmkkREFEZmhkp/Re8jolfnuOSW8nulrLuVhAQi5VZQ5lD+DNIxgC5JUJeDytp5U5mK1HjaOZfRExFAWGerek1WloM5SqAw89RONVXe7A6mIQfIE6hSHQq7OaVJmBIQUCqByvFoSZMJIyOkJGblMTPQkQpEanWpytqUQKprwUsYslTcuYg2PH2FMbQxxpZAK5958qajMeqJdDm5Rd9/pCTtJg65Nfu9pH+3MPqS1appQKnCKoz7Hsw6rldtUGX3rDFvKJ4rgsqc2MCdAwTXplZuydaH+lzhSiDt+uIYQ6fy7mBFYWqSVXgM3dOloc5rjhIZdB2lHj/82Qvx/NSgCJjv+ToijUWBWjmnVQLR2qcRQexmSJRALwFwrzHmPmPMJoBfA/DF3t/5KgC/ZYw5CQDGmEcv7TAvHxAzvzTqI0n0SqD5vkIJVAUXlpkfC9vdcTNZQLdx7GU6wonL1jUvqjGshETZvUjqZcHPR+xwiCeQNtCnjbQmwKIFSqpyckyo+7OD9soANdCno/ZvCPOmqMrBlBtVTZaOL4SaVpmFMZWBcrASSGWSqSNZyKxZ022q8O63+HsFGMPyVupaD7LQ0jOtcoPUORpigYajbWOfl2oqzf3yO05qr6EqSPLKSEVKIMpGK8rBepklwvTlYNTJTZ+M0KopJ/4GS1G+S9B2+pSrqZqlI6KGARXJV7dDBmTlYNV9C3j/pePzj6vLSHXm9UCgEkhDApX3VkLYAXUZuD1G/txTLATY+6XxtHJaxEu86crbRWboYpWocck3zXWnxIeYxGTvisoYmhkod72X/N+V+npW4+PlYIrSSa5GkZIszTkqhATS2jTo5pwqSahVArF5VKcE4o0NFAnkyitGl3iu4l5h8rMigUa6higUf9VKIN3eEtCXX2sRci6+9GgSu+RDplICBcTmux0SEugaAB9jvz9YfsbxLAD7kyT5v0mSvDtJkq+5VAO83MAXgMVhT0UCpYnO1HRSBXTAoCdfELkxNCANDlhQpinRKIy6FrgKABPaKMg3Pdz8U2pwPCkKVlOtZ9e1gX6qVQIxIk26oXOybB3SYlJZLCpbIfPx0dhmnadtjFmaVOoyTXc7wAas0uwL/7c1JFBesHIwheS0Iu2kWVHv/QKEmyzWRUt8TGDgSKUWQ8W74pihKz3ItNciuGSVkSXSc5F3DhE6KsKJlZ5JxliXJYWUaOjmGr9EQ0a+2b8jnecpCaEZFx9fmuiuH53TeU8UZu1Sw+tqjMaoVGVAU8XWda5JUVQEjurdZ2sroCslo2vfVyiPeIAfUqJdKZYD1kopGcE9gaSJNKBeU7rOQfOSJT5Dy8HsnzXlRUQ4a+5x5aumVALZ2KtWpsmeJ/uzKgdTzlHzg0zs7wVYUoESH10bbz5+aVfQanyOMXSZXBSSQCkj0oDu698sI9W/Yxr1Rd0hVN8cYkHod8aPow27zuOzYHsHPTnQVyiBeOmkuhxsqCNaaYxzlSeQbj9lz7W1pEeIeouPT1OFUMUCiiQXVyxGTyALCQnUVvHsvyE9AC8C8IUAPh/A9yVJ8qzGP5QktyVJ8q4kSd712GOPqQd7OaBa8NMEy6M+nlIYQ5MqIihrq95gpbrNpqMs0RFV6q4AdK4y66iZyJ0ssaK9qTpLUY6xnyX676U0yeWtQ207aiUJ1LFJoEVpQSlPrc7lKUR0Rt5ppWQRK+DYAi0NYPiY1OVgRAJJW8R77Zs1dfNcai3y6Qgp0fB9BLTG0AFKAMoeiku7jLtp1xCfWvP6wiDYrJmyvVLJv+/vJfJVUhIE1RhZ6ZSUFHfMWoVy+oaBcmd3sAJ95RzIzyXpeNg4jhRzAUqghQDyLbQTTrWhEJSBcINLgBuA5vizex5pvTZcfQHUZScSg9ci4P0nUqGXytdJwCWr1F0FkzAV5lw/ExlJE8RKoGquSNWd3ABX3aQpB6POkZpylZpg0W9QeQm0TF1eE+mWcJYr2YG6/Hws9G/Ji7qLVtf3cn1UtEkx+5PHXhKCy6559QbVH0cb6FoMMnsuaTxEZX+AshyscFWzEvVL0xg6ZB7VkAP1eq7ZS3GzfGmL+LyolUDixhyUdB0FJHWYMbRUNTPJa2Jco6gKQYgSKGfv7wWh2TWdq5fpPGAbisWoBBKRQA8CuI79fi2AT7T8nT8yxpw3xpwC8FYAL/D/IWPM7caYFxtjXnz48OHQMe9ocOnv0kiuBOJO5+vCMhffTFZr4jfQsPlMRq4inIzRZ1JZNnrY15FbIXX3k6II6kIC2GBJcy2A2iQ3pOOJtNNPe3ewKS3ii7BFiVAY39BYmEXJmZmkQtIZYhjoKIEUmb28MPqyqSJgI9LSHUz6XvJNseZdJlJBXIZXtRyWl05yckvj70E+PSEKJ7pfqveLKSNFJGv5CDoBsfR+JVAR1TVpF2aSqzqXRx5rVFHzQhNf8gSSEtocxtRt7wGdiq0nUEVy5I3Ni/z916q2Cv9cmu5g3rz7K+94AF//i+/Cb7z7wZbz2J+0YadNp8xzS98ins5HnZy05bu151b3uYwxVfc4QO9NMzfIVN2g6HmQlO4B5AWoL9sxjNhSlYOVpIKmq1hdGh6gBMpqUlLiNemqROWeQP67oom/xOVg7N/UmunyxEdfScClqU7dx9+T5VFfvN9wYigFwVIpKhUJv9oYWjcnTgrDyhIVRBXrYKYlWABLjI+F6jJLAsn9GO3fs2NaUiiByGA/S5PqmkgVaoXRJyVCwUkxua1J/ecLmwpyvKAW8ToLFa6CDen+t9sgIYH+FsDNSZLcmCTJAMDrAfyu93d+B8BnJEnSS5JkHsBLAXzg0g718gAv+bEkkEx1QMaVmtaLweakRh/MVZ5Ais0BYDeO/dJHSF3mlijreinL3p9NevgoCn1nK16LrTXyrkk7+aaCAqygcrAO+b4vTw0pB0sSXVkC4Ga+NGWQPHMQQgJpM9KDXopRX55lo+57gFxy6igwFOUWfpmL6NnwSAWVEqjsUiN9LzWKNA7KXmmJIyBs056mtvNOmuhKhchAGVAYeTtEtYDQrYxrdcqtqutZwLNBSiDNxlFqTlpn8HSeHjQ+/o5oroWWsNN+r2qMZSCs7U7jnEuw+Wsogcqs/AcfPgsA+FD50zmOKZUBbkAr26S6LcflSqCQUkYap1bRoyXg6T4PhR5Y/vk6S/ccTyDdc0vn4R3PNF200jRBliZIEmn5nl8CGaYEAgRqNo8s0XTRAuq26Bqyf9BL0UsTQYv4sBIVwG/YovUEsn/WtojvpQmW53p46oJwvxHgt8MTwbpyZndu05ROzytjFKCepwbCBjFEsNTG0DolUGj3yHmFByePoyi+kcajE5bIlN7rO+66Aze88QakP5DihjfegDvuukN0nJP0D7DW0BCupBbXKoE40RqVQLaMayaMMZMkSb4NwFsAZAB+3hhzd5Ik31L+/zcZYz6QJMkfAfgHAAWAnzPGvG8rB75TUXWAShPMDXo4I5yUacHmnZJoAZl1DEA11Yk4OLDqAdbmUSDpdNpeqjt2pWoHd4C6aSg8gYw+SwHY66htRZvndfY7pBxsWEqmeVeCWcdRgKXefDMT2mlya1rwQrIudK6sDDazNBEb5PEaeGvupgsCAflCyP9tTUBBbdFttkFOlvSZP4IsW2Z/cqWN6DjPGDqkm5PWYwaQf68Je+bVhteJTuFUtZVWbg4Kh9ySvc81KV7Poxt5DqDfOUatp039vfTzGldT2UBp9vgK737pPKZkwd+4MJgvr5uWcKYAf6h4lurvpFvzaq+zVEwOVmMMKEcA5KWMluCz36XvvSMfesSSPx8/vd4cW+6WgyWJnbMlxtB5YTDshb2T9WauqGKcrnMBzFBeQ2Aq/dgqlZhyU0BzgISwA6Am66vxeeomzQY1S+w9JnVOF/zOkWKVKC+zYGsRKZ/awN/LvoKA8z3INO9ltWnsiDX4/dF2VHJLhXWeQLzDGiDvDpalaWk/IVUC6RNpjlenUq0M1OV74mRwzpRAmvelqD3nZJ0+7U9HISLuwMf2DsLvRQo5UqVJrj9XimtbxBeFTgl0x1134Lbfuw1r4zUAwANnHsBtv3cbAGD12OrscRZGX7bO43lNi/ickv5WCWSMQZJ076Uyj1CXHLebIVECwRjzh8aYZxljbjLGnCg/e5Mx5k3s7/yYMea5xphPMsa8cYvGu+NBk6vaQJlYzQCGXdt5Z0LlDwGSTiIVdKVMUJmA+htHjYKIJgVAZzSszfbyjlraErKQDXFtDC3rluYaQ89esJvdwZRBj3EnVl0Zjj1u1FOQLAGLBv9O+najci8mGl9N2mXIC9PpF+N45wR0FNF1CLI/5/qZSq3AsyhD4Xzj+5YBcqKql4Ua+YYZQwMhnW2gDoit2a18jFWZm7bEyOjLY7lSRKvAGPZK9Uvnpth6AoWUg9EzqPlO9XqiW/Nq3zddEqM2vtZ3ZeuXSgVVdzDv+bvvsfMAgFPnNprHVRu5+jNpS2QyvL+Yjn2ajC0QRkZq32WKGwbK8gA6X5ffVhUTZmHlYLytcU9RNuWUF2Wy7rGN+yUsVeEEhrQsvHovk6TaeEvKBX2PGamZN70zo34mUAIVVVdQjU8J0FRT0r/XBd+vBJB3B8tS220qTAmki1/VZdqmnkdVbdsZwaJZJyZsz6FT2aAcp640sUo6qbvbyW0yeJKQGqlISSCtEuj4ncexNl7D4uQLcHjj++25xms4fudxwTjD/fDseRQJWi++kZuv6xSLux0iEihCDh68aEq0JrRAKTolOVnbTLbZBHiGWCcFr86l9h/SZX3ra6jrIEMZPU35A51vri8z5CQ4ZpLKyc5tFSsIUnO2+RaeT1cOZj9fDGwRbwxARLrUFJLGSAG7RmkTogRyDOsCpMWa0hXe1aJfvWOyjJ5j8izcwGhbD3PCKbRufiiUWvu+ZYAuK6rycPG7gym69nFyS2UMrbxfVIqrKVn1y9w0ZHrGSHHNpl2zKfYVjp0bF+YJpA3ArDLPmndmqcxs2FlP+hoi0v4dKn/QdbWRP0v2XPYnqZxE3cG8YHZjYskI8jx7/HwLCeQRJYA8803ra6VSCFKJSecaVOOUqlj8cjBpiREZ+WtaQwPuOjTrON/jEFAa3fL1RKFSoE0SALHSppkQkrxf9qe2vXk911hjY0AWi/qedlLlcRVjCzxE8sJgec6qJrVKoNZyMAFRNSmaxK5UZZalKeYUMdQ4QAnkE7OArqw2DUimDXvW00rjW1R1MBPGyr5Zfi9LRHYcgP1uWk8gn2iVfLeKSE4SpGmCuX6maFSiI2ZOnjkJADg4/meYLz4VMKnz+cxzGbn5ej2+ekwqJRBT3AJy5XwvbU+e7FVEEugSgwdzGnVO3X5ZXyagnZRJFhtikhkic7dGqJpuWGGb1EnhlZBIlVG53hiaB0va2lfH00IkV/WUQIpng0oFgelBO2WFFob6rAvgkjnSttL+cZpysLyoO3ZplVuAXFrsmKFrFGl5c6HpCsCNaQkcheqXkPefzjUUKrD8unnp3MYDrBB/hJDvpd0cOBJhYZDqSv7l5p/V/aIgX3GuOaWcntaUkHNliXxTXG1yM1k5HVd5hXQidEtj5d1puBJI28Jauw4R4aTp5AbUHlOd1zBnzQKY8oKT4qfObjaO8zc8gDzzTetriBJoQe3TVRJwiqYXlem1UgmUF3qFMx8jMPvdmnixU9ff91GY2shbo1KYOOuy7DgqF1SVqrD3H5C/Y3Un3VS1NjS8uhTreV0m1E2yalt4V+chUqwkFgG5kpV7PwGKcjBlF70QTyDuC6pJYvpemCq7ACrRDuhgJk0g++RxL02RFzJVWlEYfZOCqkRWrwSiuW1hmMlbxLN4WXKvj64cdX5Psdz6efu5aq8uLeEP6Eggel9GisoPf04E5OTdbkUkgS4xqgkvK1vsKja2Wul+W1tpTTY6RNKpkWcDtCFOVRM5rxUf9nXH8RISuRIgwBg6rwMR7eSv7eZEcnUAYjKCZ/TJGHJaIEJZhmEvUxl4V+cyPIOViCX15BcF6EzaJoUJajdK0BghA/LsPKFgG1UpQRBqoFwpSxTH8Odw1BdusPxsbyZTOTqkQnktRKUnTEEICD1cjLc50HSM4CSrkNxKyW9De7/KbJ5U4RD8vRolxrp5Q9wi3rhBftd53HIpfZZdW+7jkp6KJAsv7dYkPoq6A5zWB49Uh5JrSJtu3k3wfGmsee3+OVwY5w2jTb6RI/SyVOQJ5LccV/lFlZsQafcX10dH1knTN72WlzMWNl5TqFgBdx6bdb/q75JedDmYxkCZkwrS5Exl5B1gWludSzhnc3JWmyBIeYdFDTmboPQQ6S631KihnGMdw2t5ZzZe9k+NCrrLwcLmKKekXhmXu0SwnITvEQGnWL/qpLNeOSdW9bJ4CJCXM9Kx/UxrkWF/LiiUQP7cNjfIxISJVQLJSaATt57AfH+++j0z+zHfn8eJW090Hut2IgswhlYQro3GF8I9c21gr5s/disiCXSJweucVQbKhetnownYraqnNJMVGl66vjSKzYGiawxA0kx5a3OgmbnVHOe2YZUeVwR0FCqQJHXrW9kx9mem3oz4nkCK7HyadBpD0mLXF2byfRhOeiif+apFvGJDmBdMuaUs3wPCa+BDFE5ig8cWJZC8bEpfFw3UG05Ntre6X0Ly6OJMMnUbTtqUaQ2veaZdSlY70n3VPAq39EzhaUMbM7l5veufoyUIpd2SGipRYSmTptSXj88h7BTPIDfm1XbE1M1Ppt7wKANhKhXUdAfjhvxrZenK9QdtEO+rgfhGjtBPZU0laHNVvceKa39oYQAAeGKtqU5qP5f9WZGlGjLCK52a5dcD1HFDX9F+nZ8PmD3XtCmBpDYBdJ6QcjA3OSM7jqsOpX5xnPSgcwHAZkcJlFM2pdh409yrUWACdWJh2Es71zzHJ1I7R3mxsh2jkNxi5KwdZ4eaylMrSp9fPh5tB0Ntoop30pSWaNnzUaJK3gK8KAyMYaSYMpkO1ESL5p5pEuNVOWOltpOTs/R8zPd74k5auTG1yl8wxtVjq7j9tbdXv1+9cCNuf+3tnabQgL0eF5OgVSuB0kRlOO7MiR02GXsFkQS6xPBl3Zp22dr6xgkLejTsddUuV2No6hmGaswJe0IJLj8GqD2BCgNRR4wiQBUB2IV0pFUC0TUM+F5UGy09n6tU0HkCcWPI6cbQ9vNeJguQ2s7FM46aZ6NSAikyKZPcqJVbbsCuC3qkHUWq45jHTE+4YeLGf0PFu9wwhtYEPSolkPx58scHuMbQIi8RY9zOW4oxaks7ORkpVdq1Sfel6pLKV0lIjIea65LqSEc42589RWDbIIG6CM+87sakz7Kj6uShnQvVhvzsPdEQVnZjoCtl5u+XjEiru4MB9ft4rvSJOHpgAQDwmGcO3eYJ1BN2nKL5uk4qyIgjADiyMgIAPN5iVt2Gwn+mhGXTQK1WrObRDpUTxQ0aX0XALTGedZ9JUaZt/MHHl1Trq8IYOvfIIzXxKSRZ8zpeA1hsI+5sVScxt6oEms7XE655lWWCUoVCxwJ+qbDw/WLk7Nyg28A6tBFNZa6t7HoK1MQsoLOt0BPjRa3CVqx5AFdU6tYHwM6HgEKxXBHIujEuKFqpV/FhUiuBpMbQeW59izRNQDjh84tf/KsiAggoFXQKw2ugvh5JojNh5y3iARmZlhduaS2gJ3l3GyIJdInhyrq1XbSUtbaBnkDEoJIxtNScmM6lykaRwknT6p1fQ023tFxPpAF1ZnogLAUB6o2+VQ6EG+tJFyke2GpVYgBmEnd0L7XeF/W5mJQ2SzszgHyM1KVGqiyh47SdzBxPIOXilKU6JUDB7pe4tt+RkOsIQnXHnsb30qmHAEUpDjuXJttbzxt6AkPrncNJO42PQEMJpD1OWG5F80aV8dKUuSk7EnEvln4mI+BdkqVbSUWlTENhmQ9HwecMLTmQpkGlTNp5kQhMTQm0r87TdAcDakN+2hiQEsgnXfzSB6DsOCUgPvh8rSUIjyxZEujRszISKERR7asVpfeaPNykJv7++YCucjCU49JtngkF676pSrLw43pCT6AiYG3wOs6JjaGNG1MCck+gnrJUmI4j5UBXzGZjSV35EqFV1SvszEZJIwAila5PcEufDfp7S6OewiPR/uRxuU6hF9CIIqtbgEuPsefSl4PVCsJyHhCUyNI8POjJuu/xMYYogSieXxgqSCBGlmjLkwGdOicvDOaHuu7MdK7FYU/8nQD3fQbk/lR19UE0hgYiCXTJwetfB70Uk6JbjgzUG/2a1ZSVFgBkaqjrosONoUWGpp7Bqzb7EiyN12xgjJ5I44a3Q8X3csznlEEjL1nTtMsG5GRJpRLjngDTlEBVOViqkusSTFnyB8i7kAC1SgzQt5YPUXsAUHke+TXwmk2gXw7W1W3C7SiSlMcIn41Up+rjpZ3WkFtjrqsrxWntlCLcjGjLdxrdwZTvMqDovmdMU3EgmkdZOZjwXJVxu7rDkq8EUqwpCmKxoQQSEhga4pfAy8Gkcn+nvDigHKzaECtM6KuOYtoMtjBY593BADvPbDBPoOsPlOVg59rLwZzuYKlcCeQaw8uTEStzfRxaHOIjj57rPAZo67Kqm9dojICMjODqBo03nSSZUymBsoR1tdO0QoaznkgUCva8+oYNFfGZJWJVcPX+l9dPmsS4GMVMmui7+xSKzXAdt+rjIadrr4JY5F6CgG2W0TXn+KWuWk/LxWFPYQxdExE69atH6AasX/rOwnb+lSXFUY0PQBWTyr0LlU2AvGSwRglE8+9cX06YcLJEeq+5Ak1DzOTGzonSzp00PgBYGvbU5WCuJ5CuHEwz5+xmRBLoEoPXv+pbIttNGaBTAqUBwa2jHlBk2e1ErpAkF3VHEe2G3VXMyIIR3n5Zne1VbEooMBv2MoxzmYScE2na78U9SzYnRWfnAj/Yn2V6OWFBqs186Y2h69KYbjPD6riiJo80135ShJeDzQ96AWWJOlVfUfCgXVYO5gTESuJD6yXmbrCEbVRbnidtqRBtWqXzodPpTzFGrZeD750hzii1GPN2nstXsgjeteAyt4toAMA3WV0ZTt/zpOv6jfPCJknKjakkSVKNrzCsHExorF2NL1WvyUCd+daQx6GZ1zSRrUO+EojmJ/IEuu5AuxKIK2wIvSwRbXicluNZIsp81wRBghsPzePkE2udxzjHBZYl2jHKyFmfcBY3NmDedJLuYFpfPz4+ul09hW9RwRSOUgXRhL0rIykh3qISBXSdrTSKmUlRoJelFcEiKUsErKIxE26GJ6VZuNaUGLg4hRN/pyXj5MqXQZaJk850nxdHPVwY58JOWGDnuggyXbl+Sciw6piGorI7VvY9rbRm3tpSPD7GkXAu8MdolUCy0qmQ9YiTMRcU+wGeHNcm/elZlKLygFUogRwLCkUssJsRSaBLDKeLloItr/1sKKjQlPykzHRVaAztdLXROejrfV+UMlDmP6TtosUl/9rOANqJPEmU5Fse9r141whpvb0f7M8yvayMoctubFJ5cDU+pqTQytVrJZDGYFuvBCKia2EQVgOvCV4mzPclyBg6VShL6F0OaH1Ni7Uo29vooJEJS8/qc2mM+HiJJqDbtM8pggJjrJFkRbL2ZV3PJuyZ1xrsO6Vnio2ZxryeG2Rq2/mmidv1rKvZgFMC3e9+jxtkvSII46UxGhNvoG40AOg892ht0JDUaaKbM2jfliYyk3zqjEKgTPT50hNo33wfy6MeTk3xBOIGtD1x5yivM5uSSJsLIOCJjNSWntIYAUE5WPlOagz56TiJr4evUNKWXBceSS1VAvFnROvhplFvN7qyCd8xNymmMeOld1IXA1DibtTrLi+qlEAhasWLUL82SCDBPABAvVaS8m9x2IMx2jJyXfdIvqaoVD2FvhTfT9wB3d/NN4bW3DMa4yDA7oKSd7JEsP23icCYH/RwfkPXMEenBKq/i6oczOnMplNGacrBeGKHhBNS1WJVtqrwqNzNiCTQJUbrJCQmMJQeODxQqs4lyM4Zo99g5TQJKcvBgvwR7N+jzTegqPdMoLqG3JBX5fDfMPJW1B7zDZYkMMuZDF+42bdmkszId4b/BgUEvYxK4nSZL8NKXDQkEAXfAMTSXXtcgWGvNLpTLjTzQ/lGhMuEdQbg7vUAugkd45CsukxULw3r2JOmEGd7/UBJrGJh59IFxFY9EFLmdjHqAXGZm3FLY6RjDCEx6BoOe6n4mXcNMuVyaW44LPZUUZIl3BMI0PqjsHulNNZ2TI21mxdFmayTeQ3ovNOlFKkJvjp8G5TGv1QOtjDo4dDSEKfOe+VgnnmyPVaoBCqMY4a+pb5KHhmpUXw1ysEEJCYvcdF4e0hUh3Xyx/77mk0t4M4ZPaExtE9uW0NpmcoG0HVlImPoej6UKT55Uky3NngJAkXcUBM73eSKtgy0Hl9TCSRRK/GyZABlqbYsecTjBs36QN6KMlPd5vslT2KWz75C1TNxjKGVKhsnNhcq0tL6HbPnl70vNgknr47gSmzJPbZjtD/p+VgYyJRADlkiID8JnBiREjN8ztGWTwPA4qgvJpx4YkfnecjUkVWSK5JAEZcQfv0roC1/CMvaalhv2rSrNo4so68xhiYTapU/Qjkct95TrwSSkW9l0BNar6woPXM7nsizWE5GT+pz4GeKZyxSVTYpS0oDSXl5Bp2LTmUDdvmzwVl5aWkIeSRp/JgqXxVFDTzPfGnr0rXSYvrfaWLbPSeJrCMeEYS0WZIsaL7fRpgSSKgEYJ1B6mshucc2uOplKdJEV8qk6SjU5nUkL+vSEbPVcewa6kzeU70/ilJxUzB/L2nHSetlQWSJzBOIb+I0G2JeGqNtEa81NeeEmKYcdFIUas8jIu0TwaaHz9eEfs8qPSlgnx9mOLQwxBOeJ5C/4QHscyXyH/P8FEI6rGk8knpMti9p9R6qSKG4oS8kjarjcoP5YbdPFy+JA3QJD3s8WHcwrUm2LjlzMUqg2pBbFtv4PpOAUMVSGCeRJk1a0QZQQm6FeFlWx3L1q0Jt0CgHkxhD5+6aB+gSJov0/CrKkvl7KVe/2j9rFJWUTNMoWFz1tmzv4Mc2vUqJPfs47ieq2zvYn9ImAPYYd4zzpWqma050yBLFHoyvB2vCjl0OAddPO1VsBPoOS0NFaSJr9lBbqMie4ZC4bTcjkkCXGK1+NsIJ1m13pzO5ki68PEM8UGYpgNJ/qCwtkrysk0Jfj0oTkKuYEZa5pVCZL7qtuRUdoDwzSVV2XrkBsvdZV8fKfYSA2cbQtNj1yadDWw5m3AyxRgkUUp9Lz1RIHfbCQJfRByizoScIAbCOWNJAxAb8fUULZqdznCKjR+U7QRtpYTCXO/NGmDReW3qSCjbShMILAOXd0pplJ5qyX3supcl7qr/ujomn8NlolLlJlD2MLJl1HmNM9d6HdOdwjaFlJaR+AC3dvHBCTEcC60tI+XXv6prWRuTwFvH9UmW1PNfHmQvjzmN7QpUIfyfFZKTvPybMDhdFk4yUGpT7ipROErOMG+pNgVwJtDDoLt9v9QRSPPOGJRWk5WD+xravLD0l8limUiD1kLuWh3gCSZ/DXpaokgrVcZT4mMzebNIcJWkn78NRv2qNof1ysC4SqCWm1KyVpGSTeLHUHYLt74NMHm9I5zb3uCLAW83+dJqvqMvBSiVQZ/LD/qSEhlYJZJXY0nfMfZ8XhPfNIUs0SiD23dcVXchojBrfs0oJNOwhL4xqnuJKIOl19OO2aAwdcUnBM3oVMSOq0dW3RHbk2QqFCGCzUhppPM/o07kkckmucNJO5KT2AOSqHulmhB8DsBISjdeRY0qmKI1RGt6SrxIgZ68bSqAZi9Qkt5l8bUaDYJwMsdwYuq1do7Q8zpq86kwGAVtLrW03ymucZQaKzfI9yUYEgJpMo7IpQGGU62XLZGVdqI4B5EberYG+VjGjKN8B6g2niiBw/DpkNfqp9+yqN9LCa8gzsGIPARaQ1aS47HqoS08LTpbM9orylQZAeLtstRKIb8KFAaNTwqMoY1B3xDTGe7dmkQp2HA1PoEmBtY0J5kufmuW5nogE6mdCJZBHEKpNYVVq4JaMbedmzv6sj5MlxioFsbCMiR9H13r2M1/HGICcaK6Pr+M8aTlY45mXriesRGskVinYn1ryuM0/U7M2aEiPSrVReqMYM3u+rgkjfXewUGNoP2aTkHBcJaotSwZqJZAkJqriBnaftQkd9RwQqAQi5QvQnYRvKIGqjq5d80b9XksVeoAbc0jUXu4Y7e+kQDzfURIWqgTi91Xu02N/0jum7cK7OLLfSVIS5if8ALkSSBvf7HZEEugSg0/kqs42ZXBblXUISxl4tw6gW8ocKo3nGf3QVs95YUQlLlwJpHF+L4xX1qEyu9MqS1wJrpbNl6oHjDFodbQXBLapF+hPU5aMy24b9t+XGf5y8CA11Bha659VlYNplUBD+eLkbmDkhC5X5/TE5WB+ICIN9gtns6QlPkZCI3A/CBmW5+rsvMG+V19DlhheAilv2w7UPjgaTzCn9bXke5laNq7pDjZh74peCi4nMNo8nKQEIVcPAN1rSlMJJFBFMIJf1ykJTnewoHIVhbKMl7lqkhiU+BB3jizk5NY0ImczL3B+M6+yxCtzfTzlk0DeRg6wG35NO2RAfg0Lb8OjyQ6nyjhq4pEtmsRYltSkkbzpRVh3MGl8V52HbVwsYWfk8y5fl4V+kQAj7QJUCuKOmDlfG+SKmSqmDGhT7qjtZ9yDcdlJLKhbKpt7646Ys689J6kIc6IuZvXcpjHK5uXxgNxU156Lk0B6BaG+A6+ihKx6plLx8+F7HfVToRKIkR5B3cEUJcPc1w6olUBrHebQ/LhhoBJoTfj88+RESCMgIiQlpBN/n0d9nRLIt9aISqCISwq+Ida1YDfVhlHcNpBnbemB7ljo2709FAuAk9GXBRVc4aRRKrjZYgkpVrh+GwHEjMYYOk3l3boAOLXi0ix4wY4B5KUnvJ0vHTfdGNpUC59GyUPIjbuJ1nSOqwN9uaKKZwLFhE5eK4G0ZtI8Uyk2effah2u6g9Fx+iybsrQrrYk06abCn9skfjGkjFS3iFeWnhRFbYY+EAbvNXGE6lyF6VY5Fuy6Z6n1cRJ3ZtSW1HhZL4nvk+8bI70efkchoLs8hpMlUgKDsuyAsjuYox6UkQqNchUFWeonFcSeZanOL46XF3apD3xSoRrfpMDa5qTKEq/M9XF2Y+KQULz8mSCdaxyCUHhMUwkkn3u15ZZc4ciPkyTGHM8ycdm6wcKwO0kVqkQjGOOSOYBEWWp/1iaoOgURNfOQ+Hq0kQOAoHzPNGNKjSeQRslePYcZ61LZcc96qazboQ9OwNMYtUkgQFYOxo1/Q5RARGJq1CickJQmZzKuqFR6q9F1kKiwW5VAnUpWlMckzs+uGIB7kEnLLQE3JhIrgTxSlxSIXUqg+jiofHr48yo1a+Zzju4+2zEukRJISUhmqY0tpcIJvwtcVAJFXFK01RxKA866TbGwY09boNS12OTegq1VD7CMvnQzpzZ4ZmNU+W00AmndZKJta8jNv1XZecd/QBYc8M0cIGsR73tGTC8Hq5VAGrNlAjd4VnWOa2Hl5WV/OiUQBT0LAxvUSTdzAHnn6Mo0/VIGaTkYfy9FLeKN/y7LDR7JdBGQb7D4+2WP695gNTcw+oyNdIHnaooQA1XpfDNhCixA18ZamxV1Jf+6jmLattStJcaCDQyfP2aVTU7YpkXTtaw6l/FLC2QECxCgfg3wYgNqFYtaps6ItLwwU98TPysM2JhhfZLj3EZeZfeXR30AwNn1Wg3kJ4EAuSeQ0yI+qERTT6QBGkWP/VnPN6RIEZCYqb5bTFEYzPW7lRT8/QX0nkCcZBU3GvBMsqXlKpy0G0mVQIGG3HU8lIjXScBNmPa15XGOF+ZsojXLdDEhoUrcscRaF7HY9l6O+mmnSre9Rby8hI/mCsnGuyi8+ywtB8uZnYGiIqBWVMoSM4CfMOn26wKaStGeNHHHuuINlXFvFacolUA05UtVM7X9R6pTYnIlkKALGeAm1KQNRwBWDjaUl4NxryNAp5AO8XLczYgk0CUGLwWREjPVcc7mQKiIoMlEawztZIgkm03WslXTwrp86XRlXW7gKD2Ob8zUm8CA7mBuNzedT0dd9qeTqkql8XyyA2ZvfMaFqYLLEGPowhivTrz7eQI84lOa7S1MRXxoPJwogKAseUhnG0DepYpnzAGNMbQuuPXVb1LVAeAp0joWUN7Ol84FyOYbXzEj7rqhLXMzrjJSFBR42U1p1z7qXEKQXnsKbDVjrDOw+hKoHvteIeoXQFh6yhIYwPTNHAX/bncwhRLImKocjDoKakoSAUU5WM5VRzolK1c6SYN8afKoTQk017cB/trGBPP9uhwMgOML5PvTAGWJkeCdNMZ9dnVegjrlF19T5O+ku1GS3jMqS9b40lTHZUn1HE5DswOhrkU8V0XSetJVqlKV1DtJBclzWL+fcpN891xi9YsxDkkFaGLKmpzVGpRLlOx5YdAviWp9dzCK2ezvfQFZ0qoE6mUzyWB7XH3tNeVx9HxoWsRPWKwMlA1ipPGQH78K90S83Ger9g6NcjChMTQnPbQ2CPSuSJVAfme2+VKBeG5DqgQqSV2pEqh8PhaHvS0tc+PHkSeQpByMl0ECChW8cRMZgNxYfrcikkCXGDkrBdG1DtbLhQvTLAeTkAOAXgnEs4AaGV1FAqnKuvhErgmka5Pc0HIwjRJITQ74waDg2vsmoNKFjasigNmkwiSvDbVDjKHdIDVRGeSFkApA3fUopDsYIJM/VwFFovNw4pslaea22aFCWKLhkQoaHyzbnaK8Hh3vZZspJCDYYOXG23B2+4/k3iZ32Jf7jzhdSJQksD1ONk+1lltKu7nxcqaOLjXOGBXqtwnLUtL4pEGSL5eWPLtNFUD79ctb5nZVdzAvi2rPpdtgiTusmaaKTXbtC3fNExJ9Pvk2bYy+mhewJNCFcY5zG5NqY9dOAtmfjbWhq/ShLW4IJNLFHYVSXRzVaGGtMobWGQ0DrlJh1ndqnc8Uz7xxPOZkY/RNsvtCZSlXYUuz+dOeKY1voXSdtOdz4wZR7NWiLp92DWsfRl1HO0JR1AlTYLYfYzU+bx0C2No8U2VmfzpKIAVZsjiUJzG5cgvQxCjGaV4BdM8BdA+oSx2g8y3qKdYXX+VM80dnORhT2aisJAKUQD7psVCWg3V5Ak18UlfqCTSpSSCtfUJdDiY3AAeApVK5qlGlERk8EpK1frwMxHKwSAJdYjjqHBVBwAxehS/QJNdvohtsvjDbw8sE9FkbfVYUqBllQFYykF+MEojJ1SWg+6zqauVvOAXMvF+vLA1seYcqoNsTqJZXyzMa1bm8zVJeyMxQXfNP2fNRB6m6hZfGQ0ogvXeOvPyJjgFY0N4ZiNifdMt6WSpqVewYKAtbtnKzVmk5Y5uvheg445OR3e/YxAs25SWrHpGuKAf1yfROZZT/fgnGWG0u2BwlkbmHENWcwKRzSQN9bTkzTxB0ETP0XfuZrsSS4JIlsjmj1QdPmsDwy3BF195buwTX3TCFU1fHySqwz+rnb26Q4cJmjvObk2pjt9xKArlqGcBuerqUQBdzDe052GZO6d2g8faxY4RzXPecXTi+b5qMvqTrzqSovcqAgHIwrrRVloPRtZCuy7z8nLLrWqUdIHs++Ga4r3i/Jk6srCtz6zEl0LRz+c9sSHcwvxRfG+cBthwMmK3S4co+bdIZqL1ldB2quOpbmEBuzG2yeEjbRdIpB9MmCbxmHl3dwfwqArkC3lUFihKSXpKQvJy6PIG4T89I4QlE74ttpKJUAiXyUlKgfq6qtveC8rM2JZDIGJol4aTK9N2OSAJdYoSoc4CmHFyaVaZJIUsTpIk8UHK64SgNaDUmfrQgarOAgFu/rZXUi01DHfloJvanoHKQUHILkG0cQzuecINXYPbm25aD1UqgSWFEvg0E2vTw8UmNRvUkZp3Z0Ci3qnIwQXaNjw/QlYM1AiWh5LT5bMjKpnxz+K6Mvn8urYln03RVSUYqTDK12V7uq6ItB+MKIkD2vZpKIFlgW9WxS40r2RjlJRr1Zg6QbzwdY2ipulSjYmEZvC7VUOu5WCZPqtpqtuhWkECet5d4kxqwNjTUaFOe32kmshfGOc6tz1YC+VlvwD4jUnWetqRuwjYvtUeHjKj2ffCkZJ+2dJo2ZVX3QgH5bo8zoncyLz3sCNq244WBuhzMz5RLu2/xJKG2nEZLAuVF00dI221K6nXEPUSGHTEKN5HW+FhV52IEJiBTfrV5Ag0FsQq/XxolG82JCwN5OVizbEpjDC1TihJ43CtRRPlj5KRYNwHnxjb0TIrLwVKdFyaPU0bKZh6kUqI5fq2jHMxXAkm7VdK7YZVAOkUP7YuknkBVgnYQkqBlnkBKCxVAV7mwWxFJoEuMthIXaXvzlG8Aheocf+GVbjYrabLQTLZNri5esJ1yGr1ZMxBGpGnPpfGYKVgWEJCbf1JXAEBWx9qWfQEEZJ+Rb75tOZhuc++cq8x00nkAGQlEZRP8vBqlja5FfOEEFFpDWfFGxAuI6/py2UbfbQUsJBWcNsDygNj6CMg2B1w9BCiMzb2sqKRVcXsHQy0JHGYMLfZIajWG1ikVpHJ1p0RDWNfvB+waBZFfoiFRbnHvIUDmZ6PpnuWMr7zsmk0+nRPQlU0GkduF70khK6nxFU7TxjjNEwgAnlwbVwabRAI9dWEy89h+llYeENPH577/4rmGkZEjxVqZG9fIG9C1HAcgnutJQVxtGoUlLkCdIOjq5ubHaRozdCovAuTlYH6mXLouc5JQfP1aCAyJojgviga5JS4HU3oY8uvRlcRsLSlSxkMOmSNoze17OAH8+e02HXe6nomuof1J3e00HZnCjKFdpWi3Ssz+TBNlApkn7qRKoIYfo7wM2h6XVmuKqIOZH6dMuknGWh1tf6+VQLIyfjJ6B2TzL+3tFoY9saLHTVbpqiqA+jsFVVUI4hufGAfCPFB3GyIJdInhmGRWAbGsvbl6cjXuwit5oEONoduUQFKlQgiZQ2OUGigDbmmMtvMOBYFah381SaXsKNSWgZWcr1WpMOWYcV5395F0zmicq9UIufuZ4goijQktYDcVA6HvC1BvsqqFUGSEWGejpFlsf3Gyz7BAvt+6yZJm5+3vA2UbYCd7qHwOu0pW6uPgZEUlrYpDOxjazRZlhoRdtKYqnLrl+7ykRlT+0NLRApD5D9nj5MSnP89rFETaZgNc7dVF5hIZqlXKOONTJlkq81St0XiuPxdQE4SazYsl3+yf5Uqg+gGc69d/nqkE8shBwG7qupUl9qdWQewqSxQGr/yZUhDOQNPzQTJnO+VgmveL4pNZvi25u+6HlINxhSggX0+aJfwdm82qAUAqnp/azMYHvbS7U63XNCAVrJP2fM1NdBf4BrDyH+ogqt0Ohsr75Xj7KMrB+HE9STlYTcyoKg+oBGeoKAdrU7ILyXR/TyQlZmyyQOZbCLjJKnF3MO9dqcvBZMRMlvL3UrCXYs89EX3dKuJmsipLk87OXa6/avfzVB1XjmdB4QnEY/OuLqEcPgkU5K8qiPd8YhyQr2O7GZEEusRo9VRQGryKJa6NDFN3oO8HgZoW8X7r1a5gqS5/0HUHa3a2kWe/U7ZJVU0mZSalMN2qDaDOmGs2Bw11juB7tS28gK5jD2CfqcLU/577d4u6O1iAWZrvCSQ93u9sBcjaUQOourmolEAsMJAFFPanWzYlC+Z4pxSRMWSDnO2eA/zNoNTPqbVTirQczFMCSeYbR3WQCr6X9/7LO2+5rWilCgwA4lIcPsYezygJVJiN7HyAH5Oa3OZzqNDkVVvyxxMYXdePewJp1KEErpiVE7P2Jy8vknZY0xqhU2c79ZrHS8+6iLQ2JVAZQAP1xm7UtxveNk8gtxzMlgDPCtj9BgWD0mi4K4PdVlIraw5RNN5JSYkmAIdY6GdJ51xPpAKVTIUYXmuUQMNehkkha5MNtPsxdpJ2U9QNmg5r4rmwhcCQJbjQiFGkxKLjCaRUAnV17W3zsdKVrLpxnqTRi7/mAfU7LSkHSxOdJ1BRzcM2jpIQA35ziH6WiEoneWmnmFjMW+YNRcka9xLSEiz0U+rVlyY6FRZ/n2tipqs8zn0+kiTB/CDD+Q5jaD7fS5Rl/vkWBrJ9FODuLYf9DMbIq0XSRO4XRccAPCkpIFq9hB8QlUBAJIEuObg6R2fUxjYw4sx30cgwiZUU2g0WLxNQbtj10nj3ZdVksR2TXKUSSGtA50jIhT4HjfsV4I8iGSNfePlxbffaMRhXPLP1GHknDFmm0o6xxd9DyOaHeAI5QZ3KL0pHDgAAu/Top93ql0Z3MMExbeafIgUWO5fU36thUK5QbunLBDx1TkA3J43vC+AaDAKy7nvaYKJZxy4/F1D7qsjIHFdZJs14tfl0STaB0kwvvwYaFSUfX1UOJtygTXwlkIJUrDYvyk20es0z3CR/9hzfprygAB+oDTaTJMH++QGeOL/hnAdwN9+SLHaoJ1itLNFu5lrOFeKrIsgQk4JYowRy1XmzG3nY8qU61K5KYsSl51yloL/ugHxdrknaVLxpbHseJfNvQ60sUL/a87nKY5l6iMaYshbx04jq+vsEqRW9Emjr+yInPfhxgG0D/pvvfrCVNCy8eEg6Vkeh11d2gVNWLDjG0AFxlIa88MlZ0bk8ErNP3cGE5FGPPVNa24ra70gXHwLWr6dLCeSaNcvOBdTfbb70BBIpekz42hdyjD2X/V1ScunHRIDdy0Zj6IhLisJZoMrMkrKUQaICANxAic4nJRXqAEveSllr4tceEOvYfEAWUFAGttqMCLs6hMppizJL3MtS9NKkUyFiz9UimRZLGHVZ0WaL+OnHjfOiLgcL2Zh5CgzJ+AC3fK8rO1cd47WwlWboqE3pqNedXePHAG5duob4JEgWGj/A0pAlXAkknTfoXPoNlv1d+j7zMhdAVn4a6j3AM7DS99/PbmoUTg7RJyl/KOrAFlCUg3nzaJjXkaw8jpNbWWq7Z3Q3GyhqsqTD84GTuL2yBERdDtYwhpYG63SPM1m77BZ1joaI0Kx5btvgju5g3gYfqD2BgFoJBACHFod4/Nymcx7A7SxGc/8sDzK/XEXrMZNqyzra1gapiqVRetW9AU/LZ11SusvPRZtonRJIt8byBKHaY06pZOOlk9LyEX/tsuPsnrMbCQKpDULhmoar1jz2vabNAW1qEo0SyFc4DXvdSht/fQVQxSq/8PaP4l/+f+/Fr7zjgcZxbeU+0ioCwCq+5voyE9827xxtAln6LldEXJaKn0N/jL0sFa0vXP0GsHIwYekkV5fpy+OkJZc14USYH2SdnkC80kGlxCy/23w/E3Uw5edyfJwU3ra6JEFN6gIyJVBbkiAqgSIJdMnB1TmVn40wA1ttsITZDX4MUG6W1AGxXE7rS5LFAXHCpPGaLgROOZhsIg8pIaNzqcv3eOmJ1PeBbRwlGzpf4i5V6hReNmqWXHVSmCq41Bg0tp1LujnwO1tJ67d5e2SNMTRt6DRtqdu6aHXd5zbJqUShU2foy2N6qbxjT3mMpKOYfy4podtQKgnLEprdGBRKIKWKxfXOsCUXXZ0w2mTFgERdUpP29nwKJVBWBy6Abh4l9VtXZq7hISAlxTxyS+Yz10aidXsC0fqoVgL5hHjXhsL3mApQv4Z4Z2nXPKk/WhvRzMvBFhkJdHBxgFPnaiUQVwAQaM2UKIG0neP4uqxJivG1QbPm8TECwtihVBAmpbJXZgxtf1blYB1tvP3uYICc/CxMM/bSdpsUl/2W/5t7sQRtriRKID9BIF2/2NyrUc3TGOuE2GySVeujQuBJMYCU7NKynfrAuYH981986DEAwM++9b7W49IE1bML6DyBeqlVfMmUQPU1BBS+YHmzcUB3ksX+5AqWkIYIkms/rXSyyyyfE60aJRAXCkjVOT5RBViyv6s72EUrgRRmzW32CVJVWjX/isvdy3MFGEM7Klhl/LEbEUmgSwxfnTMM8txRyGL9B1oo962D20TF1trxyaTFXLWhaQfcpgToJgeacnWt/5B2EXX8NqTlaiw6UCmByvuclsG0ZOPjMt7TA3DbHcxVAmnlz4kfpHY8v7Q3971ENEqbkI4MGiUQL52ogxdpxsaTuQtIMQrmAJ2qz1EPSU1Xk5qkBjSbOVIdCecAzwdLQwJpO8A4KhElwSItxSE0A31FdzCtSWZhu+/RhlNSa98gPjJhF0hftSBpb8w9gTpIVr8cWd0uuzCQlp5Vx7SUMknmDadMVjs/JbrMK9/odz2DE+/9AKYrgQ4vDnGKK4GmkNTAbHWJX64i94tqU1NKYxv7ZyIjpF3gmqU43cdlbK4RxV6sXKW7HKxZKgXIzYbdjn1CMsc033+ge13Oi6Kaa0bCcU4rB6Pr/vi5DTx6dr3luGaCQO4JVB4jLu1ENcYqITblWvD1R+NTUh3fYgwdUqqyf37g/J0zF8YNDy7uTZeW6kpNGXSaysZnj4EzRjKG7ppHHeWLMMnSFpdrlL2a5EdbMw+gnvO6jutlOhUWtwyRduxqI1o1nkBuOa7cE2iO2rarlGLypC6N0U34603KJXFEq0pfOOfsZkQS6BKjTZ0jrxOtN+JS/wattK2t1bO2E5nUR8SRxisUGDxwBGSt1P0s4LBnDc06A/0WJZCU0NFkG+hcrky4uxSvbeKSdoGTKoHGea0ECmkRz0sZar8omaLHD1JVnkCZ3RBLpao95XPolOFI2422ZBskGc7cezb6afd76XebsnJ62QZGmyH2W5Rq2qj6hqFSA2Wth0ubmkJcahVQ9uMbQ2sNrzVS8CZxJAsa1eWxxn0OJR1gWhV9U8ZXB86lGkpR0knj097jJvEkX798hZO0tbG6HMzwjctsBVGbaoB7AnEl0KGlIU6d26jWwVZfBIG6pM1/DBCQ/c58rev+0lC/SkuZvHWvuztQ4bz/stJuVy0ya2y+EkhriF4Y5lei7A5G15AIw+51uVmW2G2sbX/y78jVJS/6D3+Kl5y4s3WMflwj71Rbzh9i4qioxkjP7jTFV2t3sIsyhtYogerP9s8Pqusz18+wtpnjMabqo+O0XSqd85UKEU3nLR4DGFP/W7OOaxpDyxLI3NtH9r3sT42VhE+MV8rIju/VZsotN4ZGOT6dEoi/LwuDHs4LPYHcclz587EwlHUvc86lLQczF9HVmR8n7AKnVSzudkQS6BLDLxOQG9cZJ8CSZPQbag/BA92mstFKGIczCIW2c6XagNgP2gWkR6h3Dt8Eqror8M2IIFtO5/IJwuCJS3DtfcYbmKIEKmolkNQAlYNvYGrJf1dgYH/SJqtSOCl8cNSkHVMC6czn5NmotvvVk6hffMWMwDvH7zZFWY3OUqGClxdMV4g5x0yTTEvKwZznUEaIAXDUaZOiuxORs3EMKN8BdOoc3xi6M8s+RU0hMcn0y9WkZEQdfGeyMrKiaSgvuRYNgqVDxcLvk0oJZFpMqJWmmnJCx/UekRwTaipLZR18fKHdwQ4t1gqCgwsDbEyKyjvCL7UGZB1q2ko0ARmpAOj8xwCfqLY/JYoewCW4hv2sc9PTMBqWdDxyvI5mK3qneQJJn3v+bKjLwRoeTt2qjQbJKlQC+abG6x2Gsm0xiiTudfwpFaWdNMYuA/B2TyClEsi5FoJSFS+hQ2OlsTz7qiUAwGNnXRLIqhV1yQjAXffE5WC+uqzDW6kaI4s3xP5ejHRWzRteEwCVQoTZePTSROy75ZQYKglusRKohbyfH/aw1uUJZJrPsqz0r1QCKUqaHbN8ZdJf613oP4sjyTw/xRMoGkNHXFI0DUOTTt8MwN0E9nuJqC7dz9r2BfXsTWNoeSbFbwMu9R9yumgplECupFNungrIA63WIFVK2rEAS9u9iMYoldNrDaV9Zcms6z/J681OX0jw+eeiU0kJQtos9TN+PboXgElLgCb1tOJKIJn82c32AprgxZOcCjLmDe8cZY0zlfxJWps2yBJFmYsdnzwj3bwWOmJGOka3pEZO5vBzSZ+p3JvnL04J1J0VbSjmhGSEX2KkCdgBeWLBv1ddna20mT+Ck0UVdllqS3wAss52Da8jhfq1n1mjYYkk3vE6agmgnzy/iQtlwM99lQiHl4bVn/cv1CTQoUX7+aly89hWrjoSKD54JxxAYaBu2kpPZaoD+n5S76i6tLP+TJohzljspdpEU8e+GWOzpEU9KKnXDsDVFz75LlwbUndd7/Rwy42zsbLj1JNvVGLESxF9QqgRv/ZknkCTwtTle2LSw/7sCeK8ViJX4wlk0FB8r49nE2J+QsfH1778BgBolNWFVAMANbGYlEqgC8p4CJB7gzoJU2GZUFuyT9v0AqAkgz7R2ssScYt4p8RQbQx9MUqgDOc7PIF4pYP0febnm6dyMKU6R1MO5pZ4S8vB3DWQFF9dpDPgzlPS+WM3I5JAlxiNTU8qZ+a58mUszOj7GaaujWOzVjxDXkgMVJsBhVSp0Eut6Zf0BeeBIyDbKExVAknL47QTeeEG7VKFU1OqLiPS1KUnhWmVoLd2ByuKakEPaRFfMHJLbkDpEhg0RkknIoDul6K8oAxEQrypuGmo9HnyWzDP6rxjz9WSoeg8pnw2lKbenIzUmq425wC5ioXGqC1l0KiwmmSErtZe+nzwkgRASm65GXON8arvdSTPLPuEU7dJprr0tEUlOm1eG+f+uGSZv+pcLSb0IX5xgIxUvBhlWb3m6ZIKbeTgJ//Qn+DL3vRXzrj7bMe4POpj1LcbJv75wVIV9HjZJr6NpB4JiHHeCYePURIDNL0Eu5NijdLpgFJ3GqfIGJo9U6rSh7Q7rpmqBNJk17Xku0c41+bf3XGU/y53KQfanikiPj70yNnqs//7wcec43zVoby7pZuQ1BhDWxPf8lpMUyuy7ntSDxsOTlQD9v0qzOzkTFs8xPGi6/cDaFECFUVQeQt/5kcCopSPsVE63fVM5S1KUUXSKcTwms9TIc08eqmmMQcrMVRWVUiVQP56DliCpksJxKsq6nle6NOTyMdHxwC+H548QQsovFwZqQvI4iJu5k+I3cGAXvdfidCgbdMjUgIV7kaaam35Itl1Lo0xdFtwyyXlPrjXUVVCosxGSSZkGqMfUOgz+sIsVqAxNCc+yH+oC/79GvayzszypCohqT/TbszoXEB74NlqgKqRP3tyesnxPoEBlIqqLiUQC9BUMuEyw0neT1olECBsNzxFCSTx9+GBo6SjmO+d4/h0DKYe5pCRUuKzoQRK5WUJTX+k7pIEoMXDpfNd1hs8+8E3lQvICKf6d5lixv70N/uSzW2WufOatCNWm+poaeYYm2pFCQEv9Szy33tNJs4YA8Oy7FpCjHfEBGRzVGg3LI1hJR3nd8Oi4yjbe/cnngJQ39sBfwAB/Ml3flYjmUNKoMfOWkVGG0ldd43pVgJpO4TyDY+q+2ZrbKPP6A97GU5NNqcdUo4xwFzfuBseKrVMWjbxfhxXvYuaTo4NJZBwbfDXZcE6RM+8WAnUokwb9S25e8/DNQn08dMXGmPUdo4EmmS/zIfF/uxldevwace1tV1XlYNNib3Wx7lD0Drja0n2AcALrl3Bex88Uyn9fBKo4asknE/9DlWaEqGaMBXuA9pM7xXKF0uoyztvAfCUJXolW0+UuKvH2EvkBLdbcilTArVVBCwMM5zfnEydd/hxbnMT2bzTS1N1STONURqnAG48KvF/teeqSV3A3e9xfzznGI8gBOQ+mrsZkQS6xMgLg36fZYgFElefLOHBUm/KokHHabO2fraMEzpzmE4Cca+jegMonSTL8SkC4lCvIz9LIZWdOi3iRUEgaxGfyR3tG1LEGcGjPU+bEkhmlO1fQ2CKEiivu5ZJg0X3XGh0BwsxvB72ZQonOk7qSwG4z69GkQZApfhqy+hJfCb8Z76XJZVCj3/eNr66lEF47QOMoZtScFlm2X8OZUogVzEjVm4UrKOQMHhvy7BJS0/agu/ZAZm7WdKUrPrKLWnm0A+SJPOor1bUEM5UAjXtGLr3Pf4uCjYgQN1RsNmOPjzxMQucwJQaf/rrkGauoWviq2bvffRc9ffGeVGNod9zn7PrDsw3/t2rVkYAgAefXCvHZz9vVwJ1ewJpr2H7syuLAXwlq1atCJAfS3eTAn6/tJ4qw35Wdewb9Jrvvp/Uok3KhU1bImSPa4/zjPfMV53cutpXT4mHuhKSbUqg7sSH/ek/U5t5gY+eOo/5gTU1fvycS8bxEkjAPh8XhGQEJ3THuWmorJvfy900zrJO4J50mk1wdbxX5jZiZPU0Ar4t2QcA/+ubX44LmzlG/Qwrc3082kIC+Z1IpeXx9GzM9TNV2/AGISkh08uFWdoG3FdUahV69foiMeVukpi9tJsc4Ak/Op+0FI9iBKk6py2uXBj2YIwlkKYl8CslUFYrgeQdu+S+hYBLWGs8gXLfGFo0PvuzYTg+zoG5/pRj2lT6UQkUy8EuMfzMdy9NxS7zvcbkKliwA/wbgLCyKdpgSVtRNjrUiOs927LRuiygVnbKlSXi0i5l5x0/6JGMsa1WXKr4apegN6/jpCiqTb3GZ4fAVWJSA8o2c1PJhMzbI9O5pAEMz3AGt0SVKpw8QkdDzAAycqbhdSL26anfSTpWu8Gi7FyXeaL/LksyL77UV2PKy82kAUnG0f4MmW/a3q9Z3413jnLGKPFVUcie7fjsT9/YWHKuxuZb4ftEAfvU9ubl9eHdCDUGuUA9F/ayFL00UfvFic1J2fci9YBW4dhlGtx2LhojHcdLaj566nyDSJuFg4tDHFoc4MOPWCKpXpPrv1MrgSTrkE/Mdr8nqfdsyD1LwmKbnvNezi43NMaUCZ069hIpS/iGp4Ms4eVLQN297fzGBN/16+/FLd/3f/Cek0/O/E70laouX+W1+JP3P4IbvucPGl4xfjwkLQfjnkB1OZiMtG8juh58cg1Xroywb76Px865YxznRZVQBGRNA4CmJxAgU6MA7poyLb52PIEUG+fqXDOUQF3j488JYK8jeXwdXhq2lIP5HWflZElaxUNy8g1oWZcF64Nf2ilXVKM6l6brGT1WkqYtY29Noj93xjastFljJWGrCGh8MpLRb5YDWE8gADM7hFXztlYJVJASSJ5oLZxzycvIJlwJJHx+p3keziwHa02oyzzgdjMiCXSJ0eapIDVQbnSNUQRYdJxWgaGZyNUlLj4xI21T3LLBkmYOePANyFuO87aGokXU66IhDvS9BRuYvbGor2H9qloTtO6Nj38M0P7deIeJkBbx/Htpy4uc4EChtMnSVGwMCzQXGk13sDqg0JclArIOBIW/6VGQQI1yMIlipvxOlemqkJjhHTSs39ns44qiWcooNQ2vVRgyFQs/V6V8ERvl159J3ueGqk+ggPPL3KSeNo5fnNCodHqQ1O2fo62Zb5a5ziCBGmooWSkt0K70kLQA5oEwEL7madZXTXcawC1l9I975Kl683zPw2erTYtfDjYNV++bw8Plv5GXHiJcrTYSeFM1uwMSGdGtcOw5cYNso9+49oISTb88FuhuKkH5OW23KT4fdm1A/C6uC4wE+j/vewjGAH9z/xPt55niWUbPwJv+4iMAgL8/edr7Xv79kqtEKRaQkqxtRDp9xwceX8OhxSFuPLTgKNoAtyEFjVHcXcn3tBOW1bveT7NLVvvZpekOJiHu2+IGH1e0kECtvkrChKSbFJNt1gF9l8W266GJ8wAFudXihSdtosCrLkiJLTkX95mSlT81k9Vd17+tHIxMm9c2ZPN2P0uQJjpPIOk+io6hc2kIJ94UReoR6M9vkjXM97UDohIIiCTQJQf3VwFk5WCNUiahmoL70gBCc1I/qBD7bYR7e6i9c3wSqC/vGuVvHMUMe5aIN5vVGPn3EnYuaFMCzRqjv4Gh42Qy/Pr3WZvHcV4rgUKNoVNvcyBuA+55AomVQGmiamefF4UT9EgXJ8At7egk31o2qhJfsKLl/QJmy/ebJVpCAs40CYyQzjuSDR1X5wCybh1+e3PNHNU0KNYbLko2+z7JKhmjX+YmNQ7m2V5pZq5hDC1uLR+mLm3M11PucZ11TatxSU1X27LlEiVRUdjuhb7njrrkT/GeuN3PhN2wnPm6Pu7Uuc3q+eJKIL8cbBoOLgxw6hx1B2tXGwDCcrDGc6i8hlKSxS8VkpSDtW2U+hnOzeig45OS1px49lwNuKq0rg2P7wm0MLR//+OnL1Qqm/tPnW891pT/ZKMczLvu93nHT1NuScxufUI8RAm0POpV4zq0OMBNhxcb33FcGGfj3Rf6+/DykSrppCz7nVWizQkjTQljNb6GOkfwfrWsQz4OLw0b5WC+4lv6fjllf2U8JGlEw8coVbG0eSSp7R0UBKFznICEb1MCSSo4nL2DMoGsUbAA9vlNEjjkPc0jZzfGU4/jz3JSqoFkhJ+1IlGROYwUk8ZfgPt8SOOBaZ6Hs+Yqv0Tenq+7XHC3I5JAlxj+Zk5TDuYv2JJa29BMZZ0RkXtFOBt2hVmzq8CQLlBsgyWoB/alftJNDydZVJ5ALGM+ENexFo1Si67ztQVYdD1OPr6GT/7BP8Zn/OifYc2ThObTNqltSiB2b0NaxHMVS194DetnnquVdPXbKmPowiUIVN0w+H0OMBkc9NKZ7ZfpOEdpJ7gP00xDJe3o/Q2WWNXXKO3q2gT6Hh22FG92u1xPCRQgOw/pQkKQZKMmTE0FyIxy/TI3GqeExGiUMgnL3HrePK/plGTP101wa5SbtHnlnTC15WD+Bkt0rxLdvEvn869FJ2GXh615TSKtPu7UuQ1cs28OS6MeTp3bUJWDAdYcmkggS9i7/39UXo/7T63hRT/0J/i0/3RngzhplNSJ1Xlhm1SfjJQoAfykE2DL4dbHxdRWylXHGEayaIyh06S7ZMiPZ4a9DP0swd8x9c6pc+3m1X75HpXh0jPw8Bmr8PogM2AGmu+KuBysaLazFyuBHBKo9uU4tDjE4aUhHj+3WT1H9rgC/Za4ZhaKwprDZ0rlcluMPb1FfE1Ua7raVmP05tCRRAnkKZXaQEogvna2+kwKjaHp2ZiTkjne+6XqpNkot5buHezv4nnD93EUxHoVqc6e+17aXQ52MXsHXj4tGSPvoEW4dr/1gLvvsXYCGWjGsCNxNYZvjC736XHKKENi85D7LHiG+XtNID8sPi/tNUQS6BKjtWxKmcGSd3LwjHWz2mh41viApjG0JDjwa4+DTDIFm29erkLH5YWZOSlPa2ErNlB1SAVZqZBr/inJADQl/3aMgo0jI+BGgwwXNnO88/7H8eTaGB974gI+8FAzCPSvIdAMVI0xTuAXogRqa9us8WIiSBZ6rgRSk0DK+9UeUOiCFwCVMWbXuRyVTdp9HZs1+sJ3ueE/1O3vVV93dwG9IGhRyhUOkoAztJsTD76lZVOtJUaCbFSbUqFrjD65Zc8lINNNe5evmcc02tHLyKOi0Bsntm1EppeDUfa0JuvF5WC0Yb/Ye6XoDqZVYPpqJY28PfHILTrX6bUx9s33cXhxiFPnNyulCs9cz8IhtgH3vxNQK4He+7HTePz8Jj5xZh3vLzuREfxNdF8417T6FirVHnScZO5NE/f5OLRo/VR8U+J6fB4pqfBUAWQdpHyfLcCWS1F51NUro4qkm3UeAqmVisJUpYL3eCTQtKYBkpjNITAk173FZ2ppVPecObQ4xKHFISaFwVPrtWrBL5MbCBoocE9AOgaQe3WlLO6dSgLlbuJNWu7Pz+UnFQCZEmhai3jAKoEujHOHoG0rm5TGQ7Uy2l5DyVoOtKwp6gRy9z7AN3gWK5xaFCIS4t6fN3qC7qzO3kGjSm+QhIJGL97+CwCedWQJSYJGmaV/LsAtm5KVgxXqVu/cx0nVBMCL24JaxAuUR/57DdjYHIDIE2u3IpJAlxjNTKqg1eDUevuuAKtoZNmA2WVk0zyBtN2mNGx+1d5YWIYzaWSjusdYBz1uhkiauXVIBWVpl7TMzd8QSxbfto3jfN+SClxefc/DbtDuP4dz1WTnjtOXwmpqmwmFmdKmfAZaN8Qi6TmTamsWKBaIaGvgHb8IoSKNB4Hzg15ngNU0UKZNlobQVQT6XhmpvINh/dnCsDfTlLA6F38OJc+8799QXguRIoWZtQPyFvGN8jjB9WhV9c0o0/LL3ACpCXXRyHgFt6PvKCOza0r9u9S/wf1O04kPf76RBn0Af7fY+CQKkbz9Xmk3xFJDfoCvefLuYNOy5WcujLEy17eKnrMbmJTlu9O60PngG3D/OwHMxPf0WvXZfY+5GwtfdSgmZovaxBcgAkMwX+cucTfXzzrVlH7cAABXlt3R/Pbk9fjsT17OLC1JouO6kjk+2QEAC4MezlywhMizr1rG4+fbSSDTQlIPshSTvMCZC2NMCttZ7N5Hzzrj9jfD8oYNron1sJ9hXUA4+z5TS0wJdHBxUJFxnOyyZehunCdtAKJVHjcTJtnU5GytGNDFrQTfB0+iBOKlRdPQ1ia+dY4SxkMpIwYAdKuVfWJWQcD5cZ5YdcSVQErDdnuu7tjcfw6B0hi6Y9/WtncQGUO3XA9JdzB/Dhn0UhyYH+CxKQQy0CTv5crUELU9kSwpelmKLO32E7PHhaj03f2DxlqDv2PzAnPt3Q4RCZQkyauSJPlgkiT3JknyPS3//5VJkpxJkuTvy//+3aUf6uUB/2XtpfqOXfKWyF7WVnCcnyFSSeP9bK/Ub4PJR+VdtOrfJYoZv4uWtCVyKJvvdMQSlPsAzWeDJNMUDM4aH198SVny2NkNHFkeYnHYa5WD+6TdoJc2zuWb4lUtPNXlYPbPWZogS7sD6WpDzMa4MtevxvdTf34vfu1vTjaOqxfeul5Z0rkgRHLabvIqM/DzF5q1zclshZ6nVpjm/cDhZ+bkCsKmiqXrfrd5FiwMezhXmhJ+8y+/C//Pb/xD+7nYvDEnyLxcTMlqQ5Gm7CoIoOrodHptE5/7X/4Cr3rjW3F23X9vphhDzyA/24hPabtcX4IvCdj596J5vptkdQ0ybWZZ0KCgEeRPKY3xSpkkHlHVeVqNobuVNg0PLOn62uoJJOxEplWJtmzmaH2tSKClQVUOJi0FA+BswNs2E2Qa+vEna6LEV6ZMazmuaREPKJQK3j2bG3SrDtu+2y1HbFPuN/z3d+C3/u7B1vMAemPo1k44U+a0NvUVdQjrZwluPLSAU2e7ysG4SsGqWIg4esUzD2GcGycp5HvuSbtA+mXJkk2ZfwzQVAIdXiQCo/6evAy9OpfSc0urPK7XhxlKILaZrcclVwo0jfK7Valt3o8+9s3b95jHcL6vkrTbIh9j3aa8+112S7uFiZaW6yH396oV6iGeQJIkxjg3DRIoS5Nu/yzHGHprlUC+9xPh8NIQjz41nQSqu4ql1bnESiABwe0eY3+6lR/CtU9xv+gYAEzNJjeG5msnmWt3rS27GZ2RRJIkGYCfAvBqAM8F8IYkSZ7b8lffZox5YfnfD17icV428Bd8SQ2mP3FJjBqrcyl9KdqYYUDfKUXWKQnuuaSmX15GT+ad4y4amm44gJ1MVA7/LEid62cY57PL1QA0zLWX5ywJ9NS6zNiNMD/sYW1zglPnNnB4aYhr98/hE6ebLWL5c5gkCfbN9XHmghts0nfli6BEAu5/L7+8SNoBimeJ9y8M8OTaJvLC4Mfe8kF8z2/d1TwXy7TXSiqZuoy/X5KFsGGEKLguFTHjbWAK050JdLvoCMrBvIC4L8z2NuaoXiaWZ/PvtTDIcH5jgrPrY7zl7kfwv971sfZzeQEP0EECtWzMAB0JJFUCtXecsj44H3rkHD786Dnc8/BZZ4M1rWMfICPgNS2s/e9FJFrn2uBJn+f7ZSeRzo10MVWRMvUYL0M8q2yK/PFqJVB3CTOh7d2SGkPzeUYqVfcD736vu2zSD06lSqeiUSo8RQl0brPctMhUQIDdhAN2A06Gnz5GPTtHzfWz0nvIXSdCE1VtneOk3becDWdPoATKm2VXpJ4AgF/8q482j/HKJqUt4p1ysI65ZlwUlWKFQKauBxdsqdSFcd7qW1SexiXEyvWLCJVPvm4fAOATTO009u6XvNNX4RIzojivaJAXFNsAZTlYeR84uci7kgKyd8X3zpF0WAXak5/T7rOvopI2kqjG6JHiNVkiT3y0oS1p6PsqacoZefwKdK8pTZ8ufXIGkO0D2jo6SgkWwO8CN9vzZeI98/y4WeDxYS91vbpmjtFL3o/63UogPyFBOLw0xGNn11uOsPCvo8a6gvZESSIzePbV4poESKXcKoUCXfFA1QjILwcTlOO3KYG64qLdDEk66SUA7jXG3GeM2QTwawC+eGuHdfnCN12dH/SmmhISfAPl6sGc0foPaCs965Yk+oF0SJYdkJYx+EoKGbngm0lKCJ1mVwBdOUiaJCoVTF7UHg7V/ZIsop7yBZApgRwSqCSdPvbkBVyxNMKhxWFDTu53LwKAffN9nF5zz0X3fcCCVEnWlWCMNWp0O1vpiU8A2D8/wMakcIJZfzHgpF3VIlMg5QxSAvlKNknpSUswV9Udzyz7M62qPon6zS/RkGRuU2985zvmmrbOOwtDO7d96JFaheYToc1ysO5r4c+HklIrwFVTSTf6rUbemZ2n+IaFe4r4qkOgJp1mPYtthK50Hq1IoL4saCEekL7X4qhuSy09F1A/8zNVbHmLsmxqaYxbOjLspShMfU1nodoQK6+fr0aRbhxb/TaE77/GmLQ6F4vxaUNcFLaMa2WujyuWhjhzYYzTa5vOfN0FIoFOndvA5sS0tpanMsNDSwNcsTTEQ2fc8il/Xqs8ywSb9iBjaI9YtD54AjLC28wlSXPucc7jvf8aJQVAnXBmZ8t5900CtVA/tDSY6VtE8y7nWOYHGc5v5tX8dMuVVu3EiTsigbnZ7ZzAm85fh2SlKk3yYmFQX+srloa4oiSB+HM18RpliLwfvUYZUs+ShqfVDGWJr+bVegI1rBP6ciWQr0jhWJmzz8xT6/UcPm7xVVI3yhAnndG639geY+hMFJf7+5uqQcyMY9uUQBLyk8eiSakGko7R9evsTkpyDyeOK5ZGTnlg27kA15JDEttTCbq0gynQJOAkynl7rvq7VeXu4kZA7nEz3zGP3AXqpFokgWbjGgA8xftg+ZmPlydJ8t4kSf5PkiTPuySjuwzhZ0XnBlmn6ZT/QEtJhbYgFbCB2fH/fRe+8mf/uhH0+z4n5D2yOSlw5wcewave+Fa88U8/1Pq9pmUqu74XnxQkWYpGXbrIQDkwQ+RdD+kiygmdOcEmH2iSdivzpRJoBgnkBy/8fPc+eg43X7GIQ4sDvOfkafztR59wjvPjiX1zA3zi9AW8/va/xuf/+Fvxc2+7r7ofQ/aX5we9zmePQBsz30xWYhgKuBPy/vJ6HP/t91Wf/d3JJ53jeHcwDYvvewJJyUhAaQzdIt+XvM8+QcjJko+fvoCv+4W/aZCFfonG3EDj71X/PjfIuueaFq+jxWEP5zYm+Lm33V99dvvb7msc1/bsPrU+xle/+Z34/B9/K37qz+91j/HfScW7TOOjmnRpS+/UI3Q2Jjl+8911+chvvefj9Xla1EMHF+wm58nzY/znP/4gXvfTb8ejT7kZujZCV6a0qY8hI1qpiScFf6Q8IFPRf/vbd+H7f+d9jePa1hTTQdI0lUDTA2i/y4nKc6DFgJau36Nn1/G6n347vvrN72z8W42yrosogVa3iFfI29sMr89uTGCMTRjcXJY2vedjpysSQQIiGX7j3Q9iMy9aCSRSKxxaHOLmK5bw4UfaPYHoOqZlllimBKrPJ/YEapkP+UbpfR8/g9f99Nvxvo+fqT5r66DD8c77n3D+Pv9efP2X+XrYn64xdPs7OW4h3qgcjEyTAbR6e7QlFRaGPaxtTCoS6DlXLQMA/s1v3VW99743FWB9iLoSJj75xuenDzz0FF7zk2/Dv/i19zSO8UkgTr5ds28O++YHOLw0xAcfrp+rSe6Vg0m6aHnXYySMvdqar2xOUcz6TRA40fwLb78fn//jb8UX/MTbGrEJH2Mb6fymv/hIdey7H3jCOabNtNYHqat+9+/rtSgvXAJD4wlUXcOezBzXlnbXv9dlkIJ51DeGLsf4nf/r7/Han/zLhp2B3+13IGheATTjSl6Kt7Y5qWLf//7WOk4Z566air6b1O6Cq1i6vDCBtnIwGSnWZhp+eGmIT5xZx8eeWGs5qqkEmhN4fNH5tGrWkFI8wL0eoaRu1SJ+kuO/v/U+fP6PvxVvuP0dzrxQNwGo3xdaR99z8km89if/El/y02+fatK/WyEhgdpmJf9J/zsA1xtjXgDgJwH8dus/lCS3JUnyriRJ3vXYY4+pBnq5wA/mSLUxK7DwH+i6TlGQtU3c4ACwgf4d7zyJd97/BO7yg54p6oFxXuDOex7FPQ+fxa/9TbOso00KKu2UxDt2ybKizY0SMHuxaZJAslrW1tahyomLNlidHaC8iXxx0EOazFYCtQWBV63MVX++5colfNmLrgMA/OWHTznH+Uqglfk+3vvgGbzjvifwkcfO4S13P9yuBOpnnc9ec3z1Z6qW45wEWrCblbd+qJ4b/urex53jxozN72fW50hi6sYX0aHUw6mtlj3ge80J3md/I80Jrh/9o3vw5x98DG+5++HGd+LnGgqDuca8Meh13m+fEAPsc79WklSEv/6Ie7/8cxG59eFHzuFtHz6FDz5yFr/33k84x1QKJ88HR7IB16pEfCINqDPtT6zVmfXT7M8+SQVY81MAePz8Bn7yz+7Fe06exjvu9wL9lmdj2O/2R6D6fMLCsCfK6PPvNexlGGQpzm1MsDHJ8SvvOIn/8dcPNM/V0pUJ6C4xlnbsapjBKjwH2srBaB1638fP4D0nT+NtHz7V6JbiG/OKjVAbqqjue9WUqUvLwdo7kVGCYHnUx9EDtiXwA4+vYWEgJ4EOlPPqU+tjjCdFqxLots98Bl71vCvx9Z9+I551ZBEfffy8Q7q0rUPiznHKtYGO80sm+Lz2m3/3IN5z8jT+8l5/zWuGqz+z+in44hdeDQD4k/c/4p4nd+9XX9AVCOCkWLfnS5vx7Fd+6nV41fOuxFe/7PqKBHq8ZfPRRhwvDHo4v5HjgcfXMD/IcM2+Odx4aKH0CbLzVKUscfwvBEqglneFnoM7P/AI3vfxp/Dbf/8JZ6x+gxLCv/mCZ+P7XvPc6tpef2DeUQKFzAU+aSdNBGnKi2rfsuY7/Af/8BAeP7+B9z/0FP6KPXvOGAu309++uT4GWYr7Tp3H4+c38KFHzuKPveewLSnmg3yVuJqblJUE6fvFvfqqcrVOTyC3lLRWUej2KaQQ2ZwU+N/v+Tju+vgZ/M1Hp62V9feSdO1tS9wBlqB938efwjvuewL3nTqHP7jrofpcebNEVlK22mjM0UtF/pRNY2iZEqjtHXv+tSsAmsnS6lyN7mByZapTwSFsbgDokqaAmwCVEottVg103B/c9RDuO3UOf33f43jfJ9wkAeCWg1E8+mf3PIq7Pn4Gf3fyNP7Gi9t2OyQk0IMArmO/XwvAidqNMU8ZY86Vf/5DAP0kSQ75/5Ax5nZjzIuNMS8+fPjwRQx756Kx6REsUn5wK13Y/NIzKi96+Eydgf6I1+Wj0X6ZBflPlHLih59ab1UQ9YSBPqFRDtZPhSx0e9ezWRPstA5rEjY/TerMlVwJVH+vub6sLMnfpKZpgmVmhtx6TEtwcMuVi9Wfn3VkCa+4+RD2z/fxRBkAGmNsxteToO9jdfrHrl3BUxcmFanRKAcTK4Ga5MAsqTWhbULeXxofAsA/edlRXLt/Dvf6XWq8yX9BWLrGN7dDoRIoL9BQAnSWWk0p3wNmv88N8pgFWE+WQZ9fztDIilaSWMEz752ru7yopcNNqQR6/NwmvuRTrsE/es4VDS8Rf46iMRJxdN2BudZjANYGWJgdajPKF5clsjEujfo4uz7GqXMb+McvvBqfcfMhR4Lvk1SAfXaTxJZk0NzzoRazdv84qTTeJ9K6iM/6vaw/WxhmOLc+cfyN/Pe0rRys7e855/LXhpkbrKYZLNBdUkBjA+BtUi2hy58hf81rIweBbiPUkI6YvrJUGkA3y63txpPWhuW5fkU0ArWSRIIkSfCa51+FM2tjbOZFpf7l+JqX34A3ffWL8NoXXI1nHllCYdBqNOx2MOzecDbUVKpylfr3uX6GvKiTaQ+VHnjcsL2tOxgAvPrYVfiJ138ybjy00Gyg0BIP5YWpvu808Jitq+Rks4UEeuUtV+BNX/0i3PqcI9V99edBoH6POamwMLTv/32nzuOmw4tI0wTf9bnPAoAqBmhTloSYa/N3mZOrH3joLDumXcFy22fehG94xY3V7wcXB07JW9u5AJm5a73+C2MvTxk1S/Hlm2oP2cb5ifObeNkzDmJp2KsINx/GuEmxXpbipitszPbSZxzETYcX8ZFHzzvHVMTTjHKwJEnw2hdc7SjGfH+wQZZhUpiZHjiAa8ot9pkrmgT8oJfi7Lou7qVn6kmWWHnyfHsMwI2hpeRs+zNVVOvCC6/b59gnjIumx5rIn6rtmdoqJdAUT6BPv8lus6eVhPnvy1xfFttz5by4RLYl8SSr/DDOMYDcy7XtuDMXxnjBtfsAwJnv28rBKM7+2JO1kspfI3Y7JCTQ3wK4OUmSG5MkGQB4PYDf5X8hSZIrk3KVSpLkJeW/+3jjX9oD8Bc2ifu4H4hI6xT9DhpEAt3HgjffOb5N+QIAG6zbBNCcVPzNHBl4zRxfi1JBYgLKyx/scRIlkBv0VLWsgrKkkExKzkpqJJ4vQHur2JW5Ph4/v4kf+L278f2/877G5OcrtwDg+oML1Z+fWQYXB5kv0LT6cno+Vub6uOnwIp5aH9dKIKccrJsUIFSbzdCab3bcgYWapDq8OMJVKyM84pXU+FJtIiK6YK992SGhVAJ1PYfNLnVhpAIndT/y2Dm8/d5TjWesSR7X8wYFSB9+dHaJhqTzVvu5JJuDZueSxUEPm5MCDz+1jsOLQxxcGDaktG1dfgDgwXLRveXIEp44v+FsutrMDAGZeb2UjCC0kZhLwx7WxwUeObOBQ4tDLM/1cZYRtW1qryxNMN/Pyu5N9v9/8JF2EshX2nSrlZrPk2QzZ8/lSp/Pb0ycIOcJL/humw8BS9r929++Cx895W5eqmOE73+I6T+hba4hpQJ/7h711i5/TU6SpFFeNG2ss0qSph3DxzjsWSVwJ6nQVm49zisl0MpcHwdKohGo1adSrMz1cfrCGOO8XQnEQeVjXHFwcUog93mSK21ZswJmKP9/P/go/qhURc4iFXw868hiyztpx+J3WJSQW4BXDjbl2RhPId4IlYqwtRzM/nSVilYJeHptszr2YKn2esJTAvkbni7yeFbnokfPbuDGQzb2+MBDT7ExNo2h27+nuz6MC5cQHojivNlr3uakwP/7lg825rU2z8hp97hWG9flYEQYnzpn1wSf0HLO1bJhv+WIjdMOLQxwZGXUMPNtU4m24fDisNEivnU+FTy/lRKoJ1NG+l42ALA86jkJkrZjTEs8vzEpnO/Rtg4BAcbQXpxSecVMimov9LyrV5x7N540SVpJKVNTCdRdHkvH+cooWld+7C33YPXn3tEg8abNbctzPQyydCoJ5JdQS/0+uTeYuptuQAKkOkba0MM7Vz9LkCb2uNNrm3j2VUtYGvZcv8qWmIj25x974gIOLQ5bEwW7HZ0kkDFmAuDbALwFwAcA/Lox5u4kSb4lSZJvKf/alwF4X5Ik7wXwXwG83kjafexCNGrZB3LDUF6ilaWJTGbJzkXdA+5mJWBthsEAC1KZMfQT5zerDGPXZk40STY8gaRmra4SiE/k04+xPxv17AJZYZsRahec7grCzbcv+QdscP6ujz6BX3j7R/E//voB/N0DpxvnAdzv1c9SvOEl1+FLPuWaKjg+MF8HJfQ8+R1JqNzqqpVR1Y69rRxsJDSP4+PTPhttEzL5qgC21vmKpVHLhs4l+3QtmGl8GYyRkgpugDXpyBK3XQ8um/7C//o2rP7cO/H7//CJxnHT1EPUPe5P/VIGXwkkVDj4qqO5fjfp5xNiQF1+mhcGBxcHOLQ0wBPnN50AxjehJjUTtaN+5hVWdcDLrRqdXITlYI2yJE02j90vam+8mRc4uDjE8qjfqgTypfsLwx5OPl5nlPw6/baNtGSO8sst5gYaMqL+bHHYw1mPBDrJxtgWsNO1/4O7HsKvvOMkfskrIWvvlDa929zEM8lVlYO1EHaWBCrw+LlNzPUz9NKksZluC6C7guE2w3tJFtVXsVUqEaXai+a1M4wE6mVppZbUeAIBtinAmQtjbIybGx4fFEfwrpVt117kkdSyvkpL//gwqxK+zRx/ds+j1ef8+Z3WRplwy5XLLWVu9qe6oQQjJLuOGeftZtyEYY86sk0vB+Nfa2FIXRknWCrv1YFFjwTKrcLZb1ISogSi6/XY2Q3ccmQJVy6P8H6HBOomLwDrf/RE2f3TjrFwytUkc0HTOsFNmP7ZPY/gv/35vfixt9zjfq8Wpfi0Zzf3ugiN+rYznTVpn2Blro998wNHycLhl/sAVo32jEMLeMXNh3FocTC1+15X178DC32sbebV5nrsmaGLVbNOi3hKYgrUKN73ItXsrGOANkV14SipfBLI7zQljcsb/m9VgwibKNg338d1B+axxozVuSqKIGrYMKUTWecY/TiFxcs/9ecfwdvvfRwffdxNtvjrAyFJkrJD2BQSqPAbMUhbxHueQJKyrjY/POFxXDkLyGI9Hn8lpUH/hU2rBNo/P8ANhxbc9SF332sAmGfJlEOLA1x3YB6f8Joi7HZIlEAwxvyhMeZZxpibjDEnys/eZIx5U/nn/2aMeZ4x5gXGmJcZY/5qKwe9k9HIpFAbRUHrYHrJk8Rmlbvb+bZ3m/rb0nRu33wfp876i4392WYMfebCBDcdtpme5iLlmgzOCvSr7zW1XEUXOMq6g5UZPWexkTn8a+XqPgst9ixpCVJX5vp4hKm17nn4Kef/t5WeAMB/+pLn4798xQvrf2e+LiujZ83fpFKGN0kSrMzZYIKeMU4CzWvKwcqv3GjnK2w5zq/HvnmmBFoa4sDCYGqGqJbvd/t0AG5mWdMdiF9CSUvUdiWQ3bDdf2qtImgeOrPeOM45ZlgTi6QGmKYS42bI/SxRk5Gjfrdsd5I3gxBeknKoVALlhXHKGxsm1OUc8HCp8CIlGw8IfQlzmtoWrJ0+U965NEa+fjkY4eDioMx2zt4QA/Z6PPCEDd6uXB41NnXtxtACosq0lFsKvKnSxC0jWSyVQB9+9Fx173imbFrADtSKUv/Zau2U1qUEaisHE5Zn+uMblu11T53bwOElm6FvJDBaAuguQqeNfJd2V+JjlJa7Fabd8Loigcq5keZwjScQYJsC5IXBk4LOYhRHPOW0orY/G2ulskW81HjZX5fnmBLoifObuHb/HL7yxdfhnofPVopOP3nk49lXLsEYOKbX/nxNJdQaT7tZnQiptKyLeDu0OMSplvKiNiUwdZw9uz6uCOsDJTlIPmb2PXPPKfUEmmby/ujZDVyxPMSzrlxySsO6rnv9HQcwBnhyzSYKCoPWuWBm6alnGEzx9VqpBv5EWSboq3T81vIyJVCtlFkf55WKanHYw/Jcf6oCpk0J9PnPuxJ/9q9eic997hGr5jm34SiRpUogv6NsXhjH1Fhles9KhABhi3iPLFka9WaWg7U9v+QJdKokLvbP9xuEWu7d50Emi/N8tT1Xljx+fgMHFwa4pTTYp2RIm2eXaF1uPFOKWLShBCocMu3uT7j7gGkt4oGyTfwUM2OfoLUJJGnSlJE5AkVP06xZ5ofHk3fSpJDfqIjGeercBgpj35NDnlqvVR3Z5yTQEMsdz/NuhIgEipDD96UYCkzX/NpSwG4Ctd2mlkY9JImVtvWzBLccWWoogXz5MycwnrowxjMO201ZM5D2O3YplEDKgLjhI8CyG2cujPFzb7uvRS5pf/pBhWijr8xU+qSdWMJomhvHZebTAwD3PORKEX2PiWnYx7yFiPH2g30yoEwTK+MF6vvcNIbWlYM57Y0VUlp+v/iG9cjysOyC0iQ+eIZT0w1Dm20IISPbCALKVt714OnqM78Gvm2zlKUJzq5PcLq8r7w8g8YHeB4pASaDkta8hWluKkhZBlipf+1twUxD88JRU5Eq6pGnNjDspbhmnzU5P+VJ3P3vJVYdNOaojmtBKswWJRBg5ffLc31sTorqurYRBIBVZ3zsCZtFuuXKJTxxvs56A1OMoSVj9FopS4jqNn+UxZEtnXz0qXW84LoVJIlbOtW2DtEz/9tlR5o7PzBNjZY6x0xrKz/2sv8SvzdCTaLVn43KQNOWaQxwcGHY3AC2kO9kNPy+j5/Bv//du/HLf/1R5//7Bs+ATIHVNMeUBbXN7LA9jt4l2vyRWlKrBCIS6dGzG53lYLUSqA6GfXUEEK4Ekprrt3mJnduY4Pf/4SG7mSvfMdoA+eWgPp5Vbv54osWfa8TlYIzQ7WUpemnS+h7Tv9NFAhFB64NeIb8ByNo4x1MXJtVcRXPxE+dqTyD/WhCJeW5jgh/+P/fgz5miiuBvNkflBpC8Ng4vDnHEUx74ZV3TQM/uqXMbrRuyOo6SewKlaVI2erDX+Vfe+YC9Di3rK8BKd2bYGdAzS/HIqG/Lwc6XscjCsGfJjylejtNUG4SDiwNsTgqcZfe7Um+3eFpxLHsErfVYY3MvxfMKry667l3lYD4xCxAJNMb7Pn4GP/4nH2rEEG1r5bCXojB1IujmI0uNObuef+3v0jhvVgez02tWIfLsq2geYCRQ6pNAaWVdcf+p8/ih33+/kzABms+UtIOZr46md4y/U1xpB7RbSRA6lUAemb6ZFzNjPXuca9Yc0rhBbCjNn8WO0lp+TGN+62f4RJlc3Tc/aJSftvlu9bK0mkMPLQ46lW27EZEEusRoa5cLzGbZ20oSqO57Gtrk6mmaYKkMDg8uDHF4adhU9HiLPLVSPnPBmkZSzXdrNrWxeZnt7+NnASpJt0AJ1G4eWuA//eEH8B/+4AN464fd7nKTFiWQ1Lza9+iQt5UOUAJ58zgF90kCPO/q5YYMVJoh2jffr0gCymT5gedzrlrGNfvm8A2vuLEKJioSyOv6IC4HayvRKDM9s9B2vwDgV7/pZfiPrzuGT7p6BYtDu2Dx6+pvbodCo1HeRUOTbdCa5LZlzIn4+IeyTLOfJfjldzzgHdf0LZnvZ3jozAUYY0k7yqAS2pQlI8FG1VeWSEjMSdHcVBxiRrWHFgdVFyIegLf59NCv++b79TG8+1bL95K8l42NtIKo5mNcZCQQKYGAujymzc8KcH1ajl2zgsIAn2Cd09q8hIZ9yUa6aXgt8TpqK987tzHBqXObOLI0woF5VzUzzbOI/79Hz244G+S26zfo2bbybUaeU42hhdlJwCWLaU35xOl1HFwc4tBSU1ExrRxsfTPHz7/9fvziX30U3/c7dztzli9tB4SdPj0VgbiNckt3MMCW4KSJNcAH6rKfRaUn0D6mIOgkJOh5Z5tcXx0ByAgde+0Z6SfxEWpRpFFZ/YNlGenzr91XxSpEvHZ5At1wcB5p4paQ5R6xqFFSAN6Gp+Ue1yTQ7PV7msqsXl/rzxYGdTkzEXb9LMXyqIcnmC+gP19bJdAEf3bPo3jTX3wE3/nrf984n19SR55WpAQ8vERx5Ubdjr5lE92GA4yoqto1a7uDtSU+Sq+ujUmO+x6z8ZMfRxWFQZK4MdtUJZCnbKJ/n7wHF4ZZo0TYP5ef7OOou8FxlULhjG8aaiWQPffYI/tClEDDXookEcTlLeqLpWEfZ9cn+IHfuxs/ceeH8e4HnmwcAzTXPAB46MwFDHopjh6YbyiB/K6dgxmJhWnfy363uhLjzIUxluf6leKD/O38NYnOB9hn8Zf++qN481/ej1+eUgrNE5LSzmx+qevGuHASMg97SnHezc3H4aVhwzaBkBfunD2qCD957DuUKpwo9lWSR3yukibU2wjJuUFWdR/cV97nx89tVs/MtL0Udd07WD4XszyudiMiCXQJYYxpKEskPjhtC5v16ZjhIzQlG00Zv4OLAysx9sictrr5US/Fo6VR3cHFAfbN9xvMfGF8gkVf5qZRAvldCOxxRcX0kuzXP5c2Y97auaAzU2l/1l3PZI72/rMB1Iv6dfvncfTAfKPjBClfkhmZJcAy3xfGOdbHebUQ+c/G1fvm8Pbv+Rx8yadcW52XMgh0b4AyWBx3GycDU9o291MR0dc2xpffdBBf9dKjSNOk1VQ9JLMM2Oe3YYYuIRVaNmaSFrY8JiYl0P2nzmOQpXjuVctYH7sBTWvJyiBj3jmLKAycUqu2RW0kuPaFR0ZKg29/3qBglv5MJUbcfNSfb5Ikqf7evrlBlYU566gOWkggaemJsjtYm+pgmZWDkTE0H2Pb+AC3ROfTbjoIwOtO0UL2k8H+rHfNz3pJu561GXmfW5/g8fMbOLRE5qa81XPLu9yryQYiw7iKzW+xzY9pmw8tmcg2fgK/N4K/ngB1UPuxJ9ZwaHGIQwsDR1UGtJdnUDkYT5K0Scd9KT0w2/uNe8XY76dQv7bEDY+f38TCoFfN/0QG6T2BasLW94rzkZXJJF4CeVHG0OzSS9VDgKtII7KPfLZecfMhLM/R3FGqIjqUQL0sxYEF14+l9piz10SqBPIJ+GG/vfSByLOuEryFQXu810Y4z7N7z1WLBxYGeGKNrkWz7fVCmVi8p1QZnF4bNxIGhUfa1Rv29eoch5eGmBSmUqi2baLbQOXeZy6MW0lFUXewohnbjMqSUEo+HFoctKow/bVhqidQ7pZY0b0lEmhx2GuUCDvHt3jncNC6ecqbeyVEml+q6SdMB9IYm8VD1ERFVA7WqgSa4KmSlGoYr+fNeYPWh48/eaFsKGHvV1t5XM8v4VeWn3JlyZkL4+r6HV6qG6lYJVtTCQTYdYkUQG17Kd+rU9z50IsrN/OiaoKyOOw1lD3+feY4vDjEE+c3W+esvChaPRk1anGx76bnXyb2EgooB2sjWhcGvapr5Mq8LQfbzIuK1Kkb5vjJu1I4sWjj0c1JIVIw7RZEEugSgtYcv94TkG0c/Q4wXS2lATRqdGkDc3BxiEOLA5xdnzgvfNtEPupnVaZnZa6Plbl+Y4Gbpc6ZBl+6K1YC+Wqqfj2RU8DnSzP9c9njumtSG8bQmkwlZQ+FSqA2Y+gbDs4DsBORvymjc7W1vfXBgwNaDGYFnrUSaLPxd+t2vAISiJ55b5GXbHr843yQssIhFXK9cgtwlSyqssSWEo1Zz5SfwQJq3wJjgP0Lfbz2BVcD8MstmhvV+UFWtVK/6XDTO6eNgJOU8jVJ1jI7VM4Tf/S+h/Co35WtRY58dVnKBdjNAXnp+ISOfxz9vZX5PiOBmhtO35R7Y1IgLwze/Jf3488/2CxlaMsChrRe9TdWS54yom2+BupgYnHYw7FrVwBMKT3Jms/Ux09fwE/e+WH8j7/6aIMQ8o2h5QoMjwQa9fDo2Q2sjwscXKAkQfN5cjdYNQlE/k3cf6BNCcSzeXd/4gzeXXrUAW0tjWWELNA+15BR8qQwOLQ4wKEyuG8QrH7ioySB+HzLg/zWd0vQBdIvcxUHtV6WnboDPnZ2wyF86H7MaiXdhgOsdJP7IEzDMisvBto7TtLcu7Y5wc/8348495ngK4H6WdrZwrrVXL8cMyk8Di8NG/NNlxIIQFkuyNVvcM4lNob2jptW+iAtB5um/DYtcw0RgUALCXR+emnc3MDOh7zU5F6v46S/saVmA/TvLg57OLxkSQxKHHaRbwQigU5fGFelGfy6cOuEtc0JfvPdDzbmwlb1a2kOT/6Xt1zZbDbgz/P9zJYktTV58BMXRDQT+T0/6FUlwm333C/3mXYdzrDybq5UngXfE2jslVvLS93b1U6zj2lfy59aH1fkzwc8O4O29YHe5Y89eQEHFwfYvzDAxqRwnv+GMbQ0xp5CppOVBF2/Q4vD6nkZT4rKC6w6jiV2KTnQKDM2TSsJkRLIIzBoTieV43OvWm6QQJNiuhLoiuWmsoyPsW09l8SIte+mvKyLJ6ylx/EximNz0z6/0TpFZDVQr+vT9hyUTDi00FxT9gIiCXQJUT9k9WeSTGCrEkhIAjWUQNUkZ2siAb88o3nMqJ/hkXJBX5nrt5q9Tds4zmJ6m/4Ishd8krerqTbzAg+U3Xd8A+V209XuSUiTISI0pOBCCWPbIvqSGw9ifpDhq156FAcXhnhybezU61r10Mx/FoAbYE0rB+MgsrDVE6hFgTP1O7V0L5GoUdo8gXyQEohnSHOvG8asLh/++RptKCXyZ88IFejKVDYJjDRNqsDnwMKw2rg+6ZVNNUtWelUGljbgXDbdNgdQJ5NZaLSjZs/v6bVNfMuv/B3e/Jf3O8e0jS9LE3zOs6/AJ12zjD6rrT7HvA7aFmv6e/vm+qXKwV102zpb0Xv5npNP4od+//34ul/428b3ai1Z7Qxs0SBmr1ga4eDCAM86sohRP2t4pExVAjkZpT6uXB7h/lOs9KRNCVS+d7/6Nyfxn//kQ/j+370bH/Y2ZoVfvicw1227X5xMsB5Ow1YlED+Om3/fcuUyAFeFOfG69fHvtDkp8IX/9S/xpT/z187f77Vt/IStZAGA366DrCTx4MIABxcG1r/DVw+2GUOXHWKefeVS+b0uOMcA7vw0Jwig/Xsc6oNH5V6WBKo3/Z/1rMOY62d47lXLM/89H7x0c24gI4Eou2/H17zPREb+xQcfw4/80T34tv/5nsa/Y59DOMcAszP67Wbt9jl810dtuclNhxcbKkKbIJi9WB5cHDhEetMYmpRAsxMg/iZ1Wrac1qYuEmhaCXarEogpDpeGtWrxAPPDGuft5WAA8P5PPIVnlA1A2roQtSm+6ZrND3u4YmkEoFYRt6mO2rBvzj6Dp9fGrXMoJ0y/77fvxr/8/96Lf3jwjPNv+OWWQO2DR8qOm69YcsYMWEVKW2l32zw6KVyjYEri0L+3WHoCAe2bRb/cx0db9722DlVtaDOGbrOfkJCYadK8hrPQZk68NHLJSyqxqs9D99m1GwCsqu/gwqA2NT/fLAmvjKEVXaP8pheAjSHPrk+q5OeVK6MqwearUwF3f0P3+FRLp2V+rlk+UxzTSn8ffHIN/SzBM48sNoyeZxpDl/u8Nl8gv/pgpFAC1aWuMkVPmwpL2rm3oQRSdrgF6vkNsIQOJT7qObGci717/RUvvg7zgwzPuWp55nu9WxFJoEuImohoLiASY2g+KS90tPOctEyuADeQHLTKTn15IGADcZIirsz1yzpfTwnkLWy81nb697I/acKTKoEKb+NIgdnaRl5tgj/IuoIA001XRZkD3+xS0RmEzgPos70AcOOhBbz/B1+Fr37Z9Ti01EbayZRAFGA9yWShs7JzfjkYD3p4J5YutJaDCTrHtZXGTBvjk2tesOQEc92drQA3WNJITqd1mph1DIAGcUcL1IGFft3O18tUzlrUqi5aLSUr/iIvagPcolTamORV+dI9Dzczem3P08//00/F73/7ZwCoCQNaQOuW4+7FoCB4Za6PNLXlYXzRpeueeATc5qTAh1hnnzbzT7UJbYt3ztwgw7u/73Pxx9/5WXa8VTmY6wnkXw/auNO8e3hpWGXQaXxAO2FCWUCgJePYeOalZa7uF1viJNDCoNGquG394gTEi67fDwD48KOso1hbmZCnLANq4sTfnErnTnd89fFkNgsAh5aGzG/D9Tpq8wSySqBNvOwZtnTvQy1do9zNd/e82CgTEiRLjDENoo8Iu0eeWneIuFufcwQf+KFX4eVluaEUy6N+dd3nJSSQV+5CpEjDeH1cVPHFQ2fWmw0bPCWrpKyjbU2hd/Ceh8/iiqVhmaxqvpddagq/RL5BAs0gB/zvxY+btlGSegItTEn60eVMHYJwWjlY3WUpbylxoeTOo2c38JyS0G3MoQ1SoVQClfPEwiCrMuwUO7QRTm0Y9VMMeilOX9jEuKU0gxOm7y2bKJzfbCYjgTZj3bqT3k3lWuk0G2hRAtlztZBAua8EKkmg8rkhTyDA9c3iY5wV1/jmznROyTVc9pVA3n0eCRNcfomxJHHXZiXBnz/yLfSPATxvtUEdfx1arDfrbQkumjrofom6YnrJdABOpQNgTeI/fvoCzq6PW8sZ+bpE84tfZtyMRWVKIH+eGlVq4HUcWBjgyNKoUd7VZQwNAI+dW2/8P/99lsb2vPJD0ugFaLFPEB7H3xe5ErP5jtFz1UsTLM/1qmQrJ0zbfLe++IXX4P0/+Cocu3alsabsBUQS6BKitV2uZOPYZkI5yLA2nqAoDN724cfw3o+ddo6pM47uv0UZ0KMHF6osKd9UtJU/jHp128BZSiC/NS8w28W9GSgJlUDeRJ4kCQa9FA8/tQ5jgGccWsCTa2OH+W41XWXm1X/54VOVn4B/Lm2LeP97+SUNjz61jvecfLL1XLMW+kML1F3JXQwFsQH2L9jJiy8es7wfSALZpgSiyXSWJxWhVYHVT7uN56aoKTj8YJOO05bvUTta/znsvs/e9xJIkqeVClEwsn+eZb7O+WTfdBKIysF4oDRVGq/1wWKBI5VZNsotBRus+UGGJAHOeTXYfoBFqrVr99tSyOVRv6EE8hd4InS4WoNv5tqM8vnG7OTja/jLD59qlBd0BewAWDmYqwRqGkOXnXqYL5ufjQbas98PMXVN28ZMawxtM9/uZ1wJdPTgPA4tDnFuoy4XbgvY+Ybzuv1zWJnrO2OdldHnxBaVnUy8drzSDC/QXmp5aIkrgdo71LWSQP0MD59Zx6QwuO6A9WPjfhZt73GdVZZ3LxKVkLaUkZPa4/xmrvb/aUOaJtUGUkQCzfW9FvFuV0agJuD5evWxJ9011s/Yysz1m/MG9+mi8oeFQYY0YUqgouhUUxz02gb7pd19YdlJQ/E1pQmFRJULWIKmjbyv1NTsa80zYnZp5CqByFvFNwwG3DKym1qSCkA597Z4MdE8tsDKwSolUEuL7TYkSWK7mDK1cxtBuDEpKsLlgy3JCHucq1ZaH+fV83oTNTeZobSdrQQyrhKoXB/FSqAWlQJH27GTFtKuDf0sxfwgczrBtiuBuhtzhJSD+WseJ4FuPrLU9LRs8YzjSsSDi8Oqs12rQs8jB7QenzTXPcSS3EDdKfDDj57DZl40Oiby60jK5qfWJ875/fss8S0EWpTY5Xd7+MwF7J8fVGva416CZlqcUpVnPtVUAvllZHTtJUp9bZevtnL8vJjdddY/Tq6cbcbYV61Ye4Kr980hSZLqXlNZ6Djv9t2KSqCIi0Lb5sD322hDm5/N3CDD2kaOd9z/OL76zX+DL/6ptzstRGt/CfcWkkz8hdfuq2WCXg28//LQJhCwwda++X6rV4TfBhgQ+qOkSiVQy4Z42EurDeBLy8ztfUx62mq6WnqCvPuBJ/FP3vxOrP7cOxvnalUPSM1ky3MRSUXHfc3P/w1e99N/5dSkA+2LKAeV7z1+3t3ASIIDutenzm/WZpQzjpvrZ+ilSe0J5HUHA2RKoPZnPqvaa3YdN0vldEW1uPHyEzezLDLJ9Vt5iheaop0sKc9nlS5NUgFoEgR0bw8uDNo7Yk0pWQFs8H7lipXg+6Wd/HvZY7pLoIxfXsSUUaQAeujMuuNZkAtKLcj0mYKmaUTfp5SKkhce3QegbjNLaJM+E9nHN/ddZtLDfh2Urb75Hfgnb34n3uV3LxGQW758f9r3unLZ3iMitw4sTNlwtpDp9z9+Htfut0HMEy3zhlNSIzW8T/wgaVQdf8PBhapEqKqZbwnYeemJbbs6aMxPQLvBKy/ZpT/7ZQ9SVR59J8A1yT/ADI8PLXL1a0fig/nFHVoc4JlXLOIjjzaVQFnLtZi1lk9VAonKwevPOPl2KUggoJ5P5gbd/14bMdtmnro5qQ1NAeAjj7mljA1jaAmR3jKHjvpZNW/TWkfzjUYJdHBhgHMbk+p++LENKVMkCkLALXVvN4aWegJZQs0nJdqeQ25A7yuBxrnBuY2Jk8nn5yBcuTzCyly/odzwW0rT80t/b36QYWGQYa6fsXIwWSkTUHcxrYyhW/zRrCeQvT9/fPcjzvHtzRBs4oPKdW8sS918NaCrLi/vc6sSqEmQAPU8uTDs1WqeFsVAlzF0P0sx18/ccrAW0m4aVkq/LkpuuddQqkr3O4R2J4/ay8FqEvKWK5fwpGfw3Eba8TXl0KIt4QWa5fFAgEJvisr5oXLvQMQAKas/8ui5diVQ5QM1RmGA60vvTr9kzU9Ijiezy0hbx1g+Xw+dWcf++UGl6peWC7YlS/m5eg6pa79XpxLIuMp5me/mdD+mmefiJJD4+W0quJ91xN5TmudW5v3SyW7frTaPyt2OSAJdQrSrUSSZwHbp+dpm7vgvPHp2dvANAP/q856Ft373Z+PYtStTsqJFY4GiiRKwGcBnXrGIU+c2qkm5VeGkaeep9EdoUwIMe1lFAt1cTuBnvEwl4GcqbZBKtb8nW5RAbbXi3QbP9qevEqHvRRvpRzxm3i8v8lGbO7vB9yziiEDEwqmzGyzLNv04zpQDHgkkNI8DmsEwwCTJ5XX8q3tPOfcK4CSm4HrMMBq2GxGhCbXSrLWhfqtKO3Pc9eAZ3HT8D/E9v3mXc0zbHADUz+zBxWatMh3XZgwNAPsXBhj1M8wPMo8Eavp0kOFt9/eqf+fv5YceOVs9N1wZIVECAe7msa2TCwB802c8A3/x3a/EZ958CAAaysM26TO9l3xz73sPAc3uRUSIURvp9338jPPvSkjW+UGGLE3qzeYUtddXvPg6/PF3fib+zRc8p7oWfoclX45MQcdjZzfw0hstuf3k+ZbsPDfXFZas+u/WK285jD/9rs/CX37PZyNLk6qUiq7ptHbvhH3zfcdQk8bmX4s2JRBl9Md5e3mQzHgSjXPxe3docTilBLqdBOLHHVkeNhSYQLs58UwlUDGNBJJ75wFuGd7ipSCB7rgD8w/cBwCY/3fHgTvumPnXl+d6nhKouUkl488PPnIW1x2wBCZ/Nuxx7rNbZ6LlCuJqTOWGkzxpALsJPcuUh10b6aqD4YarflMrDhqxTbsJ/aawHKxW33rdulqeDU7mLHtKIIDUwM1yUE7+HSw3323dSNsIkIfOrCNN7PVOkgSHl4ZVctF/p2dh39wAT65tsvi1OResbebVdVvznpM8bx43Kr3fzlwYY9hLqznATxLwOGpWFzh/w10pgcr5Ya6fzVQMiBILcz3Pc0u2vgI1CdRm1i5JOtP53DKh7u5gbYkqTkI+49ACJoXBWSdZ3YxRHO8WpgTybRDShJkMCxsI+EQV+TGStyLFlEeW67WvzdOKriOtI5Sg8ddzbfl5W+kvPV9n1yc4sDCoVPq+EnPa3DbsZViZ6zd8hKoxthBOXbH9/9/em4dJclTX4ieqKjNr7616ZqSRRjPaxSYBQjZm9wYY+wnvxvLys43F8sAL3rDbxvbzA5tnjG0wNgbM3hgwi8EIs+9mlUCAViSNZkazz3RPr9Vda/7+iIzIyMyoyhs10z3V1XG+T5+6cyanMrMiI27ce865qi+rqqjoh/izJyeBlPPk90xgKsXnnB1BEU78WxUvh2yGYaEuTNTTk9Vx/8ftAJsEOofQ0ekprAMxb0RMKN0c1lqdSHY32lIyObnyfyODPUHWuujm4GRZbDHsbWgqXppLpnglRSRPdIGI2nrx6MIabjt4JkH7C88LzqFmeTULImcC8YlcVHrikySQrEg32tE2wAnmRsw7g9qKGohStD2Hb1LVhSzeUlJXkVYhJ//1aPCdFkAC/HufKDqYW22EgWdKW1pRzXKyTBtk9tvsCOikDKq52wOnVvCLb/oa/uSD0WQJxRMol83Ay2USLccT/iiE6hAApbJBp5xqpQydLm49OA/fBz73vWiXKh3bAwBe9LTL8YfPuAo//7iLUfJymCy5ODQfZbLpjKGBMMHHEyzq2OD/jzAIFVr3yeV1bWAXl2io39eRM2t4xO4xAHGj3HSpBYCACdSfMSPmGBHgVfIOlhspTKDgez5Tb0oGy0rER4j/X6dJb3e60kw4Li+gJFkZY6jkcwk5WPwaMxmGK3dW5DipFhysNNoyMagPosNN3CN2c2PCuBwsbjTqZjNodfp3WNIl0hljuHxHWW6iRfC9IH1EuvI+dJgsuZhO8VQBwvfkSJAEmiq5OLoYmHB2ovOZygxZb3Vw59HFnsFmyLLR3/NYwdEmWHUBtNgEAGJDzP2b1O8KiK/J6VXU+PNQ2Q29oCsCRZlA6fKtvpidBW6+GazJn8nOg/cBN9/cNxFUzTtYDhglAIKkQnJNbrS7OLawjkdfPAEAiY1InMWWJ3hS6BJwQLjhFJVvcUw1bE97l0syCaR/l90+yQHdNUY3Sho5WBDr9GPlAiE7Ir45040N9f7LyiZ8SpHV6OKG6OabM1LnU/zHPOVdnip78jlNV7yYHIyWwJgIfIukb6FyXi7DkGGQsn9A78MCRD33hJRpsc67PwnJVDw+1M1ReiZQvEMaf24nltalj10/TyCd92Mc8QJBq+uTJHVA2LkvzVy7H+LvSt5J93HUJaqEBAcIOzXOa3wL1ee5sxomcafKLqr5HHIZlmDZDNL1TFdAKnlZHI/JwYpuDkU3i9MrDTTbveVgYq3bHXRCTYypWGKxRS0ga+JlgBdatMbhmqSuigvG8nK9VRH/zgpSnpxuAq52BwPS2ZFJy5DANzZ1vxeO4Vw2g1yGkRopxF8xtagNhIXuhTXFJ43MBLJJIIsBoEuWZDMMTpb1b6Uugm+NCeVBpXvD8UWNF0NK0FN0c6grmXndBqsaDHyRFKjFZGS6z1ITOje+7n/w0//yZXzgm0di9xWTg+VozvS6DbGnbPYvq3FWRSS51bM7WJSurq98hb+LzWa/rLfOuNINOgOodHj1++LnJWVCKnSBRT9DuDhElV7IC9P0r+J7jy+AlEBdIKTuh8fUJMs/f/YBAMCppfjmQJ/EjKPk5WIyyG5Sh03oGMGvUWzMaNWG+OZbNXkV33P8/etlPre3VsILn3q5DID21Up4UJEz6gLHouIjBPBgRlSwAX0yUjWGvuHln8YvvvGr+vvSvMvr7Q5OrzZxzQVcL3861jmKMg7LCqtHFwDqkGACaWi74nteqDel3Gql0YqcE/8sL0joztebclOR6LxFCAyAaNBOnXur+Rx8H1hp9m5hrVZSr9xZwVTJ7WHWqvHRSemwlLYRGZNJZ3F9/Hj8eQiPIyebCcykaXKwIwtryGYYLpsuy0R8XFaUyTC4WW5K+g+fug/Pes2X8PUH53veExCVgwEhDTyT4bLcsYKTuMb4vHtV4AkBcGlMreyi6yNirAvEW8SLjXrvAHEQ9qtuPJXOpRxsZgao1/Gj9/EubVedOgjU6/x4D4hYYEVJsOiZQF0srDWxayyPspfrUfhIskz7MoE0CTggfMfVTmdqYpwiSxLJtV6SVSoTKCH7czI9jKGDtTilINPLh08n+c07WUwUudG3Op9MKLIa3Xwt4jqA+2ZMlFytka8uqXBkYS1y/nTZk8x0ExYL70jYVFgs4XmMMXi5rCyOXDJVxKnlhvyOANU/M+rZs96KtgCPrynxa+xnNByXnIvN7OEza3Ls9e8OhsQGNY5qIc4SpbWIB/hmd2mthZZuzVOKs/2QSM7miAzieFwTFJx/8Ood2oYXbY3EeKzgyPdwd+DfEh+LvFAVfo7Jexmf6wtuVrJB1ETBjoqH40vrWmNz8RxPSSYQv8+lWBEunlhsEJPH0cR4+MtE0VWMw6Pjt1+ccsXOSqKZhzgvbjMCEJhAqkRrwMY3dOVHUoI6SKdlEVs/9appeWy84MjvntLFMGxusn3kYOdGbG4BoPfmIJ/SYk9n8CpM/G5/aAFX7azgvpPLuE81ruwRKMUhZGUC/Rz+xQIn/WXEIq9NsPDrO7qwJitCOlNIwLyVei8mAMAn7gvH+cuuTsg6GY6QkKgmt987voza5WEwE89eu9kMfF+YA+qfrd5/hOtmVSlH3Fy3V4clgaLLfXpU6ZRJhUj4dYTG0Cmb1GCxcWMBapG4UAB6GZ7q/bT/NN90q9VLfh7/f9oGvBh4YwnEq3RuLoOuH2jqezwnMTZMu4P1koM1O13sP8UD1aOLnG0j7pliNAzwd01NAukW+bCjGH8vy0F1PjyHB44stkFYb3dlsPTNQwv6+9JUOudWmmi2u9hXK8HJsoQ8hpIsKXu55EbaMAmkW+BFcnZprYVrLqji9ocWYh3FkPgsz8nC90PTZTeXwcnlaGKWLHMrhNfYiwmUOEdJ6lbzTo8kUBiU7qzmMdEjCaTzmWt2uhFZkwpKciuedO7FLv3cHzxNsjynyh6W1rmnijB+5Oeomyb+89GFNUyVXExXPNwdeAK1Ol048XHuZVFvtiVl/7tHFqXvW/yegOT6+sEXPiGSKE6Y/2qSAzuUavR40ZWdGedWm5gqe9o1T5XJfvzO41heb+NnHntR5N9NMIFI3nn8/+rYdd7978h2K+hksii/9h+B+ScBN93U89/oi0OHAAC/+6V34We/+ynsXj4VOa6DKBAsrbcwVnR6eAKFXhFjBQdTsW5zQHKzGW5C+hXF9O+X+E73BSb5AJ87xLjhDOL+a2WCCdTDGDqdCcT/nyYHo3oCFVLlYNG//9+//eTEhlhlArU1Jqj7aiV85MVPRIYxXDBWwFTJxe2xhiPx2EvdoI4rG+jxYph8a3VpxtAAT0TN15sySZFoze1k8N2gLfwzHrEL//r5/TgwtyobI/T2BOpEkkAJKa7G+xEIk3Qq4gbj4hmsNNp4eJl7bpbcHDKshycQIQao5nORd4XaHQzg79oda62w2JdNzr00CU4ykQbwe/ry/XN4xiN2xc5JruW5bAZffukPYrLkSpbtvKbjZPzePv67T8aZ1SYuDb7XyWJyzo7E5SZslPj64oTbXDUJdNl0GfceX8Z6u5MwyxffuZC3CibQ4lrvxJ2bZWgFBeR4oUJARxRQY4CJkivnXvWz2t1uX+uEPZMFfPS7xxL33yvWM0n4Rdis+ZRzdIqAPmNRJ48Te6m+16dJ9u0eL+CLf/g0XDgestPGikoSqOMnYo84cgGL0DKBLAZCr80B75bUwUK9iWOLOsoe/39WU3W898QyrtpVwQVjhYjHAnUjUnCzEV11V0OjE5OQMK9NGGppu9rwoXO/UlmPVwGTrdQHZwKJDO3eqRJy2QxKsRe1nzH0Q/N1PDKQuBxeiD5/E5qwPKeX/0i7I6vqbjYT+b5830+tmDHGEhUiE8NA3v62qfgQ9H+9xwOGSSG2kVSNoY8urEUqcXHIBKZmk7rWCluOJxOEXS1jJo6Sm0vKwbJmwYEcG8HzMKoqacZGo9WJ3M8X7zsd3heBgQHwxM6ZurrIJxe1KwK2wsMv5IFn2cvGWFGahHMgSzwV65zV775E4Cjkn7Wyh6mSFzHWNGECJbqDpSaB+IZCXKcu4ZR3slhvdrBQb2F3oM/XegIpp4lxKGRt1+yqJDxLdAln7TV6YbckchJIavvDDWeyOh+yGqbLHiaL+iRQnK0I9B+/FJlb3Ny0VxGDb/D52iASkov1kH0BRL9jYVy7tN7GVNmLJGV0Jpwll5uJi/u++1iyohm5vgTdPxdJ6tTKXqwZQvJZTJejSWnpj7QcY78qnyU2CmfqLTzvHbfh9//j2zigJHLDzwrZSiTvvPjYDeRbnQw/d8eR/anyrb7Ys4f/+34XlywcTxzXId6KWucJVFEYSqFfVFIOltMwgShysHiC4JcffwmKbhbXXTQeXkM+J6WkFE+gUowJFDeGDteTdP8LQJG6x7wEW50uVhrts/cE0jDSAGDXWF7K/gUmFW8VHZsSAB6xewwPC9aTyZKbMPLtxQQCorLEopsLfZUMEhi1sgvfD33IEuyybEbGUE+9cgcAxOJevQ/eeqvDE5YKE6ivf0sfg/L4+qNKM8W/n8kIU/LoZlF+X6lrQ1TabWKuHXoCJZ8F1TtHJ9MUcfmL3vUtPP+dtyX2Kt1u1EtQ4MLxAvJOVtvwoldCd/d4QcrOxT0N+n1F7kuTqBLd9LxcJpLU3Fsr4f6TK/D9pFm+m43JwYQnkMrO8ZNFZ1FA7nl9mjV2QmluMFlyFCYQzRMI4D5pna6fNHqPFZ6pTV/UeXvQjl0qc77f5wB6G49+6OWvevFkMcqOLIYMs3jnw16INyoZddgk0DlEzyRQwAT6ob/7PB7/159JTK5yYcsmA07fF4aXbqTlJXWDVXSzEUZHu9tNBFdiI7JTab0KhAFJPybQkUjL5uQEBIQTHkUaB+iz+cLkWrRPjLew7WpkOF4ug07Xx8mlBq4IJAO6jl2mSSBdYCY8gcQ1XTpdSmxExDPoh2o+lzCGNpODNchyMFE5VCsRQFg5uf3QAn7gbz6Dv/nvu3v+G2K9i2v0AeDB06uoNzsouVncc3w54mGi8yzRIS6BSpjkisptn64Mg0g0xHm65Faj3cWZ1RaeGVTK1GRJl3hfE8VoAK5b1P7XtRfivpc/E8990qUAeDC6GpN2xr9isck6NBcmqeIG5b3uS2jKa2UPtUpS9kMx/6zmc5KtpOsqqEMln0Or48t5QZfAKLpZnFppoN31MVVyUXSzEU+gvt40wRx15c4K1lqdiOTChAm0lGIMnbwv/l71617k5bJyvq0WckFyMDmPxn3LgPQ5Ku36vFyGb7riXkd9AqVKPi4h09D9i+F8Ijp2La610Gx3tZTsspfDqeUGbgs6t91/Up8EEtNH2utVK7vRzkAaBqZI0D0m6FA3HawrYo3VSqBzGTAGfEthT9x1bCny78YTfU6WgbH+8ozE2A3kW5N1zoh4+In9qfKtvnj5y4FiNGGAYpEf74G4L4VOljylJDDHCy5/7qvJuSbuPQIkZU8qdN1SAeCPn3kNvvsXT4+ML9UYmiJLSjOGHrhFfGzj8vv/8W084s8/Lv8dqidQ/LmIMU+Zo4ou76Am5GBpSYXJkot215fvMu92meywGL9GIFiXm+2gHT2tgykQJluFR0uCXRZ83njRkR0xI8WITjLuzec4i2Wh3pIbaJ5kibFLNcwSHeOr1YnGyKJbE7/v8BnEY1AgGfP2guplBSQlaP0wVnBQb3aw3kwmGE08VeISQ5EE+vL9vKiVZvIex6QiR1TPAfp3gQV4ckLdpwwSlwN6BqzYS9XKXoSho85fcSaQGIf9PIHiBXXK3KGLHcaLqkQtH3paxZKE/eaAXh3Ckv6PgfTfsEV82n0ByXg0ZKWZNQGgyMEoCX+A73HmFCl62p4IiK4p2wE2CXQO0Wtz4OUyOLa4Jv1o7jgSCxy1TKBwUpoqu4GWOplUSKv2Fp1cJLCIm90CwM8/bg/e+CvX4w+ffjWApCGv1gMnVmW/aKKQWBB1iZl8LjtQF4KwxTb/f7zSo0sqiGtcbrSxZ7IIJ8si7AugN004zW8D0DGBurJyeslUMcGkiJ+jg6jyyPM6JlRrF8uNtqxypsnBag/w5I53+zeBvXtlpVmMPdHWWWW6xKEbh2LREMnOR++Z4FI5VfNNZGBMluJGtFE5iWTndNIXGrFoCANKknmiRuZ2pt7CSqMtKerRVu+0gH08kFisNsPNiG5RU7/7kpeUTcWDK3GNdx5dlMfigUE/3wcgNMqdiyWdSUwgbzAmEBBuOLuaDUzBzUrq/njBRSWfizCB+pnXC++KvTVhKJ+U1KWhmnfCZEmPTaruHH5fvX1VAOAjv/UkfOTFTwRjjLeVj7fZHYAJlCY9BQTzMGfkddSLPaR+lmgwAASssnLIUGhpKNklLyvllUCymCCgk03rIBiRAmqnEwHGGD71kqfgbb9+AwAaE4gx3mnm1gOhZ9GxmO9bfPPCfU76B7WJsRvItN47+0d457tn8PATD0SOG+Omm4A3vAG45BKeQbvkEv57H3lZnMWmm5+mFDaVMDRV3y0gKcUjdQfrM2/opOzL6zwZ0dawleIQbJbVWKJaTKOmxtCqZEL9jj90+1EAIQOW0iIeSEqwdb5vvcAYkx2/KOwclTmkflbkXc7rvalKHvc7W2/xxC7VGFoUHEXBQczRAmItmiq5ct6Y0xkNa1rLn15pROVgfYyh+23Y42O2ks/JxLMal1fyTqKLEFUCLa5PzPOmLeIByIRrPDHj5fT+VCq68eSAk5Et4sUz/uRdxyPnxNehOEQSUmWyhp5AfS8nYVsxSFzOrz3Z/bjgRO0uBGqlcP4qxJNAse5gE0HhKS7R0nn19Zs7dHspdW4QyZz4XJrGBBLnndQyMaPrUZ7YCU68A0at3g3lYLqOn16KfQpAM18HAg+y1QZfHzq0WC9uTzDqsEmgc4he1N28k8W3leqhzisGiFHPFYnOzmoeO6t5HFlYi0gmgHRPoEqMWdLVbA4KbhY/8rCd0lgQ4Iu88GLRBQeCVimouvtqpcSL0/GTiRnRsacfOhraXkWaVwf/j2VrdUkFdWKvlT2MB+yLxGcZ0k61iQ/BBFpvoeLlsLOaj2yie2mj40jIwQxowsK4UVTZ+gaes7PY+c43AwCWvBJw8KCUHDhZbjh5WNNtIA7dxkwkIsSCdHnQHj3CjCJSyNUuJECyIu1Rvq/Ypl0YUFK8qdRrzGZ4lyhh1n7heAFFNxtt9e4n5ZY6iASBCCooi1rZi0rjhPREhWACqV2w5mPMkjhLxMtlkGGhCf100GpblXZQkyVlz8Faq4NWp6ul7utQjZls6hK6EV1/kRtLRv2R9HJQANIYvlebV8o4rCgGtOHGLH3uBRCRkenO2VcrSWr8ZMlFs92VAbGUkRr6I8QTmL2gbpYoSTuKjxBjTH6ntSChCPCAWufdVfJyMgF59a5KQg6n3hOQXmWfKLpcLhE8n7gRusDlO8oyATlW4Ea7YmPVK+FUcPhmxctl4GSZtvKaWPNS5prEfQUyrcvnD+OJB78N+a/1kW+l4qabgAMHeNR94ECqv9CYJtkX/96mlHhhrOBE3hGB+JhXfZV6oa1hRvdCJfDaWmt1SEwgIVUU82gY20SlD2mbpK7PC1yh7C8a14h/5zsP8WR83HcvDpEEWiXKwXpBMEx1Hk5xhEmgaOJTnTcKTlbOByUlnhI/rzTaQcKJyAQSSaAFnhyLe5qJf3eq7KHi8e628WIEEGWWiHMa7a5MUqcbQ/OfdXNovBNeJsPCrlKOmgTKJTyBqInqaoEXgYQsx1QOBoTJu3gCLp6Q1CGe1M0H/l6LSpH024cXI+ekFe5EEnJeG/emM4EiSaCOPi5P7yycTDgVlTGlQjDN1L8j4Ck+iRnGx1jCZypWUCcxgVKKwTtEEqigKXL3eYY7ejCBdKzqgpNN9ftU520qC6tX4q4fE0jO9YmuruZEAR2mSi5aHR/LjTZpTgSgXcdGGTYJdA7RK4iu5HNycXeyLJkE6iQDabXqctXOCq7aWcZCvRW25SRKLaZiFO02cXNQdEP/EZ3Dv0iwzK02UfFymCq5kTbPgJ515AVMoP2nVvCwl30MP/DXn0688LoXfFfg+SD+n+iuoKGrq1WsWtnDRNFJSi1ik6tLyHrrstdulvs+La21US04mCp5WKi3ZFWAygSKV7B0Bo+9IBY50ZWs73kzM7j6yPcAABNrATMtkBwwxlB0sjL4uuf4cs9FQ2fWKhkYgQzpsiAJdHIpOg4pSYXpihe0vPXleVpPIEL3vTjNPa2Dhu4aq3lHGjpPlhzsmSxGfEHaRJPMsZjum5KMECwbkQhuaVhiXiw5C4RBvnpfuViQO1nyJEtusuRyOZjCSKF2qRMti1eDRRegdQcDwiSQzqdHDdLGCg7KeYcsBzu51EA1n5MeWMuxJBBlg1Ut8Dm83emGRuMpQbvYkKhysLRnMRGrzodykKQEMi3YpARJlYITkYPwz+qXBNLLweL3Ja6xVvZCqdVKAy3NZqekyEyuCCR7uvlGx/bSYUw+d4XFkvJd8XfAlXNUL0mSGN87q3nsqOT1SSANE5jSHUze1wDyrXONuC+FjmWjmv3zJBB/R1QPuYQnhfQEMvOJ6AV17jDxBJKxTYwJlMkwmejrB52kttPl1ebVRlvGD7cdOhP53F4o9ug8R00qCAhvFQqzJEwC9U4Cc+YA/87KGlbQSqMdrEPU6+OfeTx4z1SPFiCMYaYD6U41tiETsbJ6jeOKp4ra6XZJ9ZmLjcN+EpdON9mMQyS/i6ocTCMboSaqw2S6iLGTNg29IJlA0lcpyQRKZXr4epbzd44sAODJ+zuPRhULXcJaORHztKPGvUU32p0sXkAeNBEBhOy/OBPoql1hh8h4Ekh8Xrvro+zlQtZsrKCuZegSVAS91rCexuZEJlB8PdKyqh1CJzjlfSE3UokXaAmeQNqGHsTuYJR9rPS3rdPmRMAygSzOAr2ooKLamMswXHfxeKJ1uPAgVCc9VSd60URBtigU9HNqoFQLWnKKAL+XuVscqiGvzvzXDbTHAE806XSUHQ01UzCB7jm+jHqzg6OL65HkAKd1Jye8X/r+S/CKn3wknnMDr4bGGU66yb/iRb0puElYslKpM5Pt67ehoWgLCuPiGtelqxIIwEBCUshhMeIJRG8dKhY5IcPqKwc7dAgPP7EfL/vUG/CKj/9T5DgA5GOL4pEFPStI17ZZJCJEJ6bLprkM59RyVM5Ik+FEjTzjFRHKwqsLpIV8rx90QU+14ODAnEgCebhsRxn3nwrN0anJEnWTJTskpARYJS+HdlfxztGY7IpN1uGFuvT4movJa3SsIzF2JooOctkMaiUPzXY39PchyIuA0Cx2eb3dcyMdR9mLJkt0zzDK6nNR8aJyMF0SSLzLJ5bXMV4Mu24kaNaECbGaDxMLOhN6HXT+OWmBy2QxNm/I+wr/DiVRTX2/xgpJJlDfJJAmsQUk1zxxfCowGQf4OGx3uonktLpBvnJHUmIZ/zfTnnucxULZvABcLvhA8C73knZfHkhAd1Q81CpRA2pxjbrGEKTuoOIaB5BvnWuUXS6BkWND806qhqYXjOXleBfJWcFi00k0+21CqBtHQE0CtYJNSP+Q1g18sFYClnPYOVKV3UYN+HWIb6LVjZLaNECseaXYehpHL2NoquxfgMcPLS1zK45eTKD4MxTz7BU7wk2zSNbMBT5t1BhFxLUngjg23pRCrEMifkrI48U1KnO26hElzhM+c8L7JD4O+3WBa2lkIyJZpSYLuH9j3AKB/z/dGDrsvieuj8oEEnPwaSkH0yWd02ObqCdQtHj0tKt24PRKAyeXwr0KRcI/WXJj8vhk0k6HkhvzO4ypAcjG0HHGzOwsxt76b/za3v7miLn+DiWJrc5lQOhfCoT7t2o+ORYjXTFJfqLBv9/jfRZxtM4Wot+zL7o5lL2ctvupTpnSL0kYn7dNEnA6WwhKU4RcbC41Zen3gvheF+otMtuuGvPrGnXYJNA5hC5ZAoQb2anAJHNuNSnPAKILW60crbKJxU1oVAW7IS04EOZ/YjNHNXgteWE1rKc/gmK4pmrzBXT+KPlcFo1WJ+Lzov4siojxAMbJZvCL37dHHk9UiDSBiMoE2lnNS6q0il5MINNsfj7oALe03kI1n5PfnwgCdZOdDnGGU0uz0e+FWpwJ1C8I3LMHGfj49ds+jGtOHYgcB8J2sFLKFaswCIivOyIHCwJicY4IHk/FzFrTAnYg/A5lS9+YJ5AIXvoZ3ek2FUK+1w+6oGeskJOfNVlycNl0GQ/N1+Wi2tZUEXUQG1URsMevT4eKJiHWyxPo8Jk17KuVkMswbbep+DgU1OidAdNOGLCrpnomm7KVRttIsgr0ZwKpSfGpkhfxHgL0m3YpB1tc554lseSAuC8aEyhMAlEZKU42g4KTjcjIUqvzInlcF0yg5MZMvF9pZsPUJKuJ4XW8gt2rGCE2V7WyK+/p9EoDXT85HspK1yEx38TnaYBukhsmWEPJDyVgvGy6jAOBoXqvd3Jf4Cu1Z7KI6bKXZAJp5rW0oFbcV+Q0Q/nWuUYmw1kYC0qCMNHJKZfBZdMl3LBvErlsJkyUNoTElf89NR7KZNI9KahmskDUd4sa5KtJHl1Ct+jmUplA8U202pp7Ltb5Ku9kUhMyBUefBKLONQJi89gi+CPJ5GzCDF3/96+5IEwCie56J5cbpISTgJPNoOzlpGTdiyWBxIZbsHtUpiIQfl9qIlltXS9ioDBpr2dh9vNv4UwgfVJbbTEe7/AFKHNoytcVl9W2DFvEA2Er9oQcLJdNldMk5GDB9yCKzI+5ZAJAtKsrpcAlOs6p5wDpc3bJy6HR7sqYLC4JF+8XpUW8vK+gyyKWFvk9zp+KdFlUC5e1mFQMCOMHEZ/EO5jFC+phYjHcAyWuT66x0eNv+bXH4ZU//Uj5Ozcdp3sCAUnrBECfLElLAsk9mEgCSSmeWccu2VqeIAeLd+GljF+q9yYALKw1A8N3Sgxr5WAWA6JXdxVBo60Jr42VpDwDQCwzHy6OmQyTk5Q4V2SU04KekGYcJnQorNOS0omoFyVZ0PgFE6jT9SObcZ0/imACqX4jERNPqo9IgWdr+7WVrioL9u7xAiZKjp4JpFbZg9aQlO5g8e+r0eLdwcYKjqxoJQKslIdfzTtotrtKUoFuDC0ShUeDhbzvhJciORCmx1fvSiZwVISVyvCYWLBPLK0jl2GolV2U3GyE8UX1BIq39I0HIoN2m6FUGzrdZJJVBG8ArzLsmSyi64fBE9V8Tt28ULtNST8LuYHpag2UgbCr4ERMo9+LdXRl0I5efO+qjwtA984pK0kgqmRVrebz+0ou8OL6ACEHixlDayrmBcVnQ8hVAEQ2FWSGUz6s3Oo61PSCSiGnSHElE0hJvgHRoFGa2/bZqFKN19UONxS2Yt7hDNCl9f7soQuDbiq1soeym0OGqR4WeibQRNGRtPZ4oQQIv+O0nJ0ouiwq90XZRE+WHCzUOWu2FwND3OeeqWIQdMdYvZ2keaqQg91zfAlP+n+fwWfvPRk9x5DtsVmYKrvR91+zDn38d56Md//m9wNIJnPFWq6VI/T1BBqECdQmJz7VJE/cGJr/eVbOLa/46N34yX/+n4jEDUgWuFQvIfHMrg6SJuUUKRj/fJ4c69UdjJgfkEkgyvpacLMoOFm5aQ/j1+j3/KMP2wkAuGSqJI+JIkEoPaePXTWREpeDCXaSiJ+q+VyEEaFLzqpyMLFuxRP+8WR/P0mtzn9FfIdXBl1mgUA20mhHup6aFnRMOtsJhMbQQbIxGy8EZfrKLXVt7MX3cEzppAlEi3+9vNVUiMYG6jlA+vNIMOfjcrVcuqE8EIsdgi6L2WAeunjhRKLLYjjekkkgET+IWC1hQRH7zqhdO4HkXP+0q3bg5x8Xer6pxRl5Xyl7vemylzCG7sWq7s/EjO7BZALOsGhKYQLpmFFkY3PCeikSxGfqvDupSOz1QyWWkBx1pK9OFmSk0dUvHC9gquxKrxixEIXVnujf/38/8yj5Isn2i/Xehpw6hDTjsCpK8ZgpulmN/xBL/B0gZAIBfCMnJk/dwia6gz1wahVOlqHV8SNJMeoGSySd6s0OSl5O6+2xOzCCBfiCN1F0sVDnPieiCsCrxEkmUD+Hf91CL7LrS4EcrKbQpQF6pyQ1eMk7WbK8CODBreiywNsZ9zlPVJZnZrgEbM8engAKjr/waZdh11gez7lhD2757jHsVyRPKrSsqFzYRWuq5IIxxjdMsW5p1G5TQDRYUjcV8SSnDjpT7jSfDkBUNqLHRADGGA8+p+R72cQ+lMjVLvYdBQAAfNpJREFU6EGYQOX4JksT6OdzqmzKk91iBHoxN0QLVLHgJ8YvQWoBhN/Xynpbbu7TZVNRDxfdvHHxRJiwzGQYyl40SNKa6yvUfS4Hi1ZfxX1RDZTFuVRjaHGeKktKDaLL4XgC9AmCuK+JDnQmEK84Chku0H8ccm8EJyEHi7M2XvnTj8Kn7j6Bq3dVOKuk4Mhkf3zsifup5B1tm2F5T8QNhZCHLBkwsADeda7rAyvNtnbOAML73T1eQNdH0IkpNLuOrydAKM+4/dACHppfw4dvP4qnXbVD/rmp78tmQe2y1kuWrG5A4++xlMbojEn7MoFoEpLoZ7ZI3cEAwXLWG0MDfA4Tf/6GL+wHwDenqgdSvMCVd0I5mHhmV+2s4o4jS6l+QPK6NAwkqgRSYKzgYL3VxWqzQ1qH1E17r7XhNc95NJbX29HES0EUufj6QGUCAbw6f2RhDYyFG2eB33jiPjAG/PzjLpb3c0Txt2tpPIEmFJbonim+TsQT/t2uHzHnDhnfSdZGO8Y2BoC/vPER+PTdJ3DtRePyWDXvwPe5ybgYh1TmVjxJ1eomDfN7IZEE0syn9UYb9x5fxkveezte9uMPw/ddOiX/XNfGXsQNx5fW4eUy2DPJn6OphH+y5GJ5vS33N9S4Vy1y7xrLJ5KYmQzjvpuprc0VC4rA2uAFX3sfKo06brzrc5HjAPDBF/4AvvLAXKI7GBDGD7IpTdyCIvY8HGLDBiA9dhDFGbFXITGBqh7ujvs4aYpBnIlJ9zylmnLH92AUT6C2Zq4neQIRi1yhJ1ATzU4XEykG/UC0KBk3Ex9FWCbQOUSvzdwzHr4LAPCip10uJ7t57cYs+nX83PUX48brdgPgE5KTZWF1kzi5FmJa8zSXeYGSG1bae01cYiHdUclrq+w6anzBzWK10cEDp1bw/cHCpGulnrYgVgkbR/F3fuyR/PlPFKPSOHFenIoIpLdfBpLdwdbbXSytt1HNOwmDV1Flp5qaqj4dVNNFIJTzxI3utOgjOXjUReP4i//1cFy1q4Ld44VIC2cVvi6gUFhsItiJV82p3abicrD4+O3lpaAiNP88+4VG3M94wUE2wxKbVkprXiBM6CypSaCUwKAc2/zrjKELbvh7rexyjb7GqDE+Dm/YNwkA+PFHXQAgTDqrGwRjxgwxUS3uS/XOSbR5deOyAT4/xbslRpIliuHweMFB3uEtbAfpDhZ6OISVX8omSzUZpCQ+RXt1kQTqauZ5OQ76sN+oxtDVQg7NTheNdpfcyUUNhnWeRQBw7cXj+L0fvUrO42MFp+eGUU2sxttWx+8JoJuuLipzqLmJpH4tf9EPXo4XPvUy3HjdbkxXPPh+ci2PPz7hFyfeJVUybXJfm41a2Y0kgekG71F5YSJRnVaJJkpI1M9cWmuTfNUAoOCGjTpCT6vwz4teDquNTmSeiLO34/GGWPPWmpwJlMswXBp44VEZXgU3yZDyiUkFAbW4QJHTqeuDrlMPwO9NTYABPE4qulmcXtZLkvpBSDQKTjZRqMo7WbzwqZfLir2OfcFY9HlUFHaumBvjCf9E56J+TCBN4e262HzGP1cT8xrOUSaNIeS157jMWLDp4zFAOfDL+8Sdx3Hn0SV88q4TkT/XKRZUOdhkiccNGZZkAqWNQ5GQixcxKMkjQIk3/GRR1yO2Npf3FVgblJtreP7X34+c340cBzi77Rdu0HddFAXGuBxMrMkmY0qA3D0u76Drc7avKNCk7dt08mRdzJHOxIzOA0LWlcaMiSecQp+0PgVarTF0upxRx9LXQSSrF+otNFrd1C6NQDifrKT4wo0KbBLoHKJXhfgHLq/hnr96Bq69eDzhFQMoG7M+Y5oxhrECZxFFz+n/IoiNkEp/JhlDeyplmh+LTyaCwnnVrnJCgy0/KzbCRAvJ0ytNXDRRQCWfi8jBqMktnbGebmK97+XPxGuf8xgAij50NUbpVE6jZL11z0O02FxptDFWcKQRpPRVonoCSSmDedcIIKQrF91zR/LbUfEiHeZUdDTVXk+ZaEXSRK0qA0DHp1V6E3KwWJWO0nJYN6bcASmnIsgW/49vWnU+PTpkM7zdvAkTKP4sdGaSXowJFE8C9WI4XHNBFff81TPwg1dz6n/Riz5XCh0ZCE2eVxptsmQ1GzB7+snBkp+Tg+8jMUdFOhEpiaMpRV6QZiivQyjfa5GNoYHoJiaumdeBMYZSkCgH9CxMlQn0qbtO4MX//q3Ev2NqeL20ptxXyvBV74kqcx0rONIrJb5hFNKDgsPb8GYzSR8rwLw7WGhq3E0wDnQQ1PGFeksx/49+1njRxR8+42q4uUzEF0VA57knWt6KRMJ/3n405p1nttHfLHBpkUECMy5x6VH4KDhZrMfkWCqoXmLqZy6s6VkROpTcrOzCpdu0l4LOqPceD7u4JpJAsXkj7HrWwdxKA1NlVyZO0jauAoLFG/kcQ6mgKoGPS610UD1cesV5vTBecORzoSYw+Hl8LqbI5IS8TXap7CaZ7Lrr1XUxjLA2+hhDU1tJx03yxecA6e9yPIHEk6z0OG+s4EhmdXxjWw4KD/cEXYjjsmHdmJdJoIU1jBddZDMMUzF5ESVRlegqSG4OEexTGmHcG09u5Z0wOfDqT9yL577tG9qEurzGs+yyGDKBQjmYHzBFgWSs5waNWPqpCOhMsVDSLKZIiifQcqMdmW8SRtng8cNKTMYYucaeTCCzjolOloExWkE9wgRSmEr//d1juPF1/xMhCvBrpCVN3VwGJTfL5WCdbmRv0guUwvIowSaBzhIPnFqRLaP7bXrEJCs2jQv1aLIkw9BfvgOexFhci1WIUwKlQlwORmQCFRXzxF4JjD98+tV4zg178ITLa4kAUH5W7J6mAk+k+dUGpkpJj6R2h8YeqMSSTr02c042I48L7XhcYz6oMbS6RkXZLznksrwLSWiuTfU6SmrZyQHW7Cwufv+7AACFg/sjnRDOBrWyJyt+cegqqZkMkxOp2JDxsas+dxoTSJUXAUi0oRSf019e0MsTKL1NZrIjTjQJFGd86Xx6ekHIE8lysFgSqKV5l9VNwJSQg2mYdrrPUsdwMWZU2iFKLdQOQVQmkDhvWaHu657hP/7CdXjnb3wfgKj3EL+v5GcVI0mg0DA0zVBeBzVZYmLWWslHu29RE5/1WGdG9bPEd7PS6OC5b78V//Xto4mxTE1GqvNN2CI+3cNJZTcB6QEqTwKJDWP037/u4nH8yuMvwct+4mGBbNc5q+5gRTeLbIb7FnW63AOL4qumrg9twvoq2/KuRDdK8UchWIcH50KTVTXA7NVQ4nwjnuxLe4a6NRlIjg0hB1uoN/Gov/wEXvmxeyJ/Th1TAC9yMRYyMSmb96KbS02y1psdHFU6Yi7EvATjBQIxd3JPoGYQ2/DxlNaOWaCgdGQV0HWO7Yeo3046G1iVg/XycOqFasHBaYPnLrD72IMAgNLB/cDevX3jlGreSXT50s3Xf/1Tj8T7nv/4yLUBvY2hsxmGbIb1YAKZrXXxtuFA+veVd7Lwchm5NjQ7XZlEoGCs4MhCcjzBLTpn3n2MS4PUDl+AXlYrEoarzY6MceLMEkoThZCJFt07kP0OlaJTssAVJgde85n78am7T0qDcfUa5WedZZfFpBwszt6KjhOKn6jYVpA7XK7RGdXxBByglzbWyh4Ozddx9Z99DO/9xkOJf6eXJ1B6x7noPoAxlsq4F/uiqD9VGJv/w6fuw7cfWsDdx5Yj55l0TZ4ouThTb5KZQHH1zKjDJoHOEs9/x234f0EQQ+nYpbqVC+h8BLTnFpwEEyhtsZKbZIX+TFmvSy43x2p3utoXFeCU/7/+qUeikncSfgDis3StqNtBYM67pbnRJFBwX2n04ni7Z8pmrpqPsofC88K/M6i5W0GpuonFouiFFUeq2aVcROthJYUUlAWdEKaOc70zazUjnRDOBlPl3kwgv8ciH08CVQsOFutKRY/odVRRmCVAMjgoxphuOug2jxQ5mO4aq7HkT8nNws1m5KbVhNY9UXIxX2+RA6VQDhYESp3kAi/GOMA3qZMlD0uBRh9QTM1TPiuXzcDNZWIy0vT7KrpZMBbrDkZOAulNPAVuvG43nnhFLfj7Mf8RTbJEbT9cK+m7zZgaXgsDWup9VdXkFoFOD/BnGN+kxv0Rim60jXWcOUM3KA+Zh1SGU1VJbFHHU3TDGP27bi6D/3PjI/ADl/HvVtfFEaBX2TlrNuiS1KFvbOOdRIDkBkuFaDF8aimFCSR98EJfNZ0cfNg8gdQmBSZMIMlu6BE3CGPSbx9exEqjjTd/6cHIn5t0BxP+YCKRQQnyVU8gXQGu5Gax2mxHmKtqXCOuUcsEanImUK3iYbrMpdlp7ebVz+0tByP9EzEmEC0JNB/zBKKyjsaLjpQkmRSqrvx33rJ7rjgGHDzYN05R5W0Aenb4ec4Ne3D93kn5u66LYfy+uCdlLyYQvTCgZwKlnh5Jsq43OyTTWgE12adjAq002jKJeX/Mz1HXAEAdKyK5vaMa9XGkMFnjLEzquyyYx+K91PodBr6bajFRZeuJz4usX2fRZVGMF5UJBIQJ4Xh86ARJPIonUCrbVvUgJMYb47F3RXeNQPj9NjtdvPfWZBIovh5RPYG4J1ty7etfoOX/j7PS1ltddLu+TAbF9x9dn86cFcSDRpv2jgmmdT91wSjBJoHOEiLYBELqY7/AcTw2kQB6HwHtucUwCUSl4YvN+KoiS6Ju5gCg3uqQstdxP4BenyVkGQBvBzxV8qRMAKDfV0WRZ4jzUjcvmkx53LeI4gmk0/VOKgZiYiEsOtmwskGkxQqj4dPSUJpoGBh0QpiqLwIA1nNeohPCoJgu80Ax3iEF6C2BDDsrhHrqZqebWtGLQ3RCWol4AiUrWGtN7g+jo97rqvqek6VpnGP3JSRkEwFrgDHGu84FgXTLQL43WeTnmRpDrzR6JwjVzcyl0yVMlqIafZPOO1yeoEjPCOcwJqRdbaPP4m05w2RJqv9IQhrHj8fbUQuI1uNcDhabewmbHiFZE+yS+L/fCyLQ932f/AxLXsgI6NVNpKTI5wDgS/edjvw5eSOjMIEkWzE1Ae8grTtYHGMFR75vaQn+iZiZuYCJgbKQ/VGSOQLq2txqpzOBpLQ7VsSIjwuRcD613JAt5nUSzWGUgwEBM4rAjsg7PCEeN4bWbebWmh3JVIjLgkzmDYCPR/E8XcKYV2VXugJcMeiMqjIoda3Ao50Ig3UoYALVSi72BZ5A/VpG97ou+TmGiRk1OUCRPowVHNSbHbQ6XbJkXT3X2Bh6ZgZXHfkeAKCRDa61T5yik/1T5jXRxXC5z3luNqPdsFN9/SqaoqIIkWhNA3IyYbre7pCSdvLcPt9z2Qs79RacLA7O1SPJLt33rL6DQuYaZwJR9g7xpB31XRZMIJXZG38v84En0H0nwsSPLglEZbKlQSRLRIF3KtbxN77Gun0khgK9ZMZxhOsyPY4Sz35BiW9anaS08YKxvPw5LnMFkuPDI+yJ+HnJNaykmdMi52juTSTU1aTTR75zLHEe9WuulVycXmmi2SYygRyxZ7aeQBYEqEkgStWxqsnWUk08xwpucnJNpcaLrGa4qSAZQyu+E5TqBlUOprZivO7icdQqUSaQrvuDDqpRq/wsIl0ycY0qE0hOeOkTlzqRC6aB+jmq0WMvL5Y4xgoOnCxTurL4tParQceDxx65Gw878QB+5Zu3RI6fDabKHrp+mEhQ0SvoEd0mRLIkHhxQO/bkshnknUzIEoklWRhjKDh8ofnHT9+Hq//sYzikyC4AaGUuJGNozZh6+sN34UlX1PCSH7lSHpsseZhfNQtSAf5MFtaUJFDau6zIgICA6qsJvn/zSfvw7OsuRDXvYLIUNaKnMjfE56lMIOomtRIkgUwq+hGJEYElVlZkZ0DvCuxLn3k1nnzltNx86+RgZPlD4OFkIlep5HNodXzOqiQmnIpuFvVG1Lg2fo1lL4fvHgk7gXzz0JnIn5MNryMVR36MYgAcGvLTqOrq5jRNVjRV0jOBxFxD9WLiTCDBLKXLweZXm2h105NHBTcwGo/T7+MyBieDM/Um6s0OrgiSkarcjSoR2GzEOxjSZZ2xDqaa7mDrrY7cwM2tNvHQfDhndwxlSZV8ThaSKEG+2iJeV4Are/ydPba4jh0VDxmWZALFWRFxY+haxUPZy+Hnr78Yr/+lx5Luo+DmElVzOeYNEjPxa+qHqhK3mRhyA9zbRxR1yMbQhw7hytMH8QMHvo0/+dxbIsf115dMKlDiV9HFUE0e6Rqb6Da2fE0w8QTSyMEMpMKdro9Wx48wV9PQbz4tK2zgR+yuAohu9nWFVjUuF8mP6QpnUEgjZIMYWz53or1D3skgwyDXvZamwMU7C3dxRJFpHlY6x1GvkYqbvm8Prr1oDDfs4w1s4h1T46xPMff0lz8RmbPqukzsmBxXEfDPS65HD7+wKn8+fGZNWnAItGNebozxzmzpcrBkYqYUJNR7QZ8E4s+x3mzL9/7YYvJ7psajF4zncfhMHevEJFBcPTPqsC3izxJjBQf3BpnpVjc94Mw7WRScLBbqUfYLZeLiTKDYZi5l8Y2bXLWJGdSSIj2hbFKFNj9iDK2RP6iLTSXPW6mfqbdkp6NBJkmA9gx1lZteE3k/Sqcum692zxCTsVrRp2bzGWMBO8qsNTf27AEOHsRl80fw0bf+dvT4WUJWQFaake8PUJkK0XNEMk/4IkiX/rUmbwFqsFiLNtaAfnNbdLOotzr4wn2nAAC3HpyXrWIBfcLUpbSI14zfsaKDdwS+NAKTJUcmyFodH3mHunlxsLLeJhtyCunDyrqeFSUw86yHKdcWbGxXYkwgSjJC8aYxkblxOnrLmAl0QHiraTqDJD7Di7KienkxPP8pl+H5T7lM/l4t5BJyMNL7BQSt0dtoSUYLYaOgegl1famv74eyl8OxRe510KsBQMnL4o4gCVTN53BPrCJK3SxFkukGVUrBHjDxBBLIp1CyJ2Jm5gImUgvRycVEDubmMqjmc5hbacjNcdp3PF6Ie50l30kv2LwA3AT7E3edkO8joDKB0u9rMxH1pUj3BALi3l78mLY7TasTCewfOLWCi4O21KZMoEo+h6ML/H2hXKOQewl2XnzOEDHTofk6amUP661OolNMx9dXr0+tNNBodyWj95U/8yjSPYjrqseqz2JsUPODpkkg2RHPoEGBwLjSmp1sarxnD7yDB/Gu98wkjmuvT2PyTl2HKmoXQ03Ryc1mtKyNlqH/XbyoCJglqgV7mWLkLdBPDlZRWD0Pv3AM3zhwBqeWG7hgrABAfb/Cc9QmCmoSqNXxsbDWwkSRezOlvV/xRIT0tCLEvSXFE0vnQZZ3+HsrnjdvKpPshmVisN0Pj94zgQ+96InydxHLRlj6yn1RmEAdIutTZcDJ74v67FPkYJfvqOA1z3k0Pn7Hcdzy3WM4vcJjcgFdwbpXwjRyb5q1T5hQ9zxH468qxuKRhTWcCcaRqhYRn0WdB67aVcXyOk8y04yh0y0mRglDFnZsPVRVJhCR7q7KugB6Emiq7GK12ZEafYDQIt6JycGIGdSwu1VbVkWdPi+Q2KAmNlixxXDvVCl2T3zBObMa3aSmPUNBPV9apzNLykGiqp8cRPUEml9t4q8/enekSsnP4f9XvzM16XBJ8PNEMam3p3zPKjuKJ8cIk91ZdkLoez3Bd6Sljva4L9kVLAgotF5HxMU6IrnsJrtUCcaV8FG5Q2FI8GtMVqM8QncwKkMv/j1TNiJAuGEyMVAuKYbtLcIzjFOYTcbhZHBfcrNE/L4qeSfwBBrMGFokhPtBJIGWY0ygtM2+atQM0JPigMIuMWACqZVROhMorJ71qiwL+nw2w/CsR12I7x1fjnT7oG6WBvEeUNkD1PFksjmdCowc491LuiYbrOB7FgE59Z2sVXgXQ8EgShtP8bW81ekmuhepgecVQfdGlVXZS/J3vlEdiAnkJJlAOk+gZgcnlxt45O4xANFuqSYMQvGZQpZE+Z4LLu8suN7q6jcuwbt1cK6OqbLL54yYHEx4Can3BITMhHixhIKCwgAUMEkqANH7pyQVIu+/AYsFiEqSKBV2AMZxiq5RhknxKGQQJ+NeR7Ox7XZ9+D5t7Hm5qLkzQPct49eXw3IkCWQiBwsTPQlPICUJ9LCA9XFyKertA/S2XJCeQBWeGDi13JBxeZrc0slmUHSzIeNbsPoozGMvqzCBkp52+aBrlFjzL62VtMmBjZpHy14OGRb6TMUTLCQrCeL7XMknk7NkJpAqB+vBFv9f116IG6+7EAC0beUBTbwcFE1PLq9LKa8KXfFOxKtv/MJ+/NVH7opcG6BnpYn3QDRS2DtVxNxKM9pRk2hrAgDX7KrInycVxUYvxJspjTpsEugsIarDnNJJCzi5DKS/d44Oant56gYrk2HIO5lQDkaU4ch21ApNOM1Xoar4egA8WaILAH/4mh34q2c/AoDqgRPfpNKq2OomMG3hzWQYKppElRroi4RLs93FJ+86jn/9wn689csHIv+OLnvt5bL48UddgB975C45ifGuWiF1lJ9D+55VORiJFn+WnRD6X0+0AqKil8b5BU+5DHuninjEhTzIT8jBNDT8XlA7i+k6LBXdLA7MrUqa8L0n4kkg/v+IJ1AuO5AcTAfVXFMXvPRCOZ9Du+vL5BUtCRRWVtqEBGG8hb3JOJwqcy212IuTmUABW6lNpDEDyY5T6cmcuCcQkcWS54b3IqAxYgIpiQXe/pReLV5abxvMvdlI0h5IPkOxObpoooBrLqhgtdnB6dWoNw3NOyOQNEUqjrQAdXmdzh6IJoH6P++Joouuj8TGu5f/mA7VoL15KAejjd1aiZuhysJHKhPIjTZ50Dz3krIp2ztVgpNlEc8j0833ZkGds3sZ8sYRYQL1qHoL489Tyw0pTRBMHoDun6d+pmBaUY2hAR7k6+Z4YVJ7eqWB6bIXuSeB+HmC3Xb4DN+4qN6HVAhGq7rZMZEXAYgYLJee99zUxhBxyZ/JZ6lMIPFMU2EYpySYJV2f/C6rrE+dv5+TzST8mlrGUsRoowGTTn/jRc4gXg/iECMm0J3flj+711wV+Z5VOZh4v+K+ZUByTfmRh+0EAFw0wZN0svvhckNhVNJ8pkw9gYAkcz4pq82i0epgeb2FXIbhooliIiY9l55AcYR+h0osGtk7EJhAxOeRFXuVNXqRsKfVSI/zarK7ZbTDms4yQGUC/fhrvoRn/uMX9abcmoT6wbk6Xv7Ru/FvX3oQXwzY+uo5QPR9EcQFkVC/alcFzU43+p4ZJPsu/fzHwnv+g99NnRO3mxzMJoHOEmKRWl5vkeRgQFTWBdBc94HoZtxE1sFplmZdfmSFSOksRNmYxeVgus96068+Dr/8/ZcACAOJhK8S8RpVORi1Mt+PCcQY41KhTldSERMU7R6Jqn/6xcfgn28K9f+1sis74phUN6dKXIttysA4m04IadcDJCmZQO9A/5mPvACf+4OnYW/gxRL/nqkd8YAwqOj1PApuDt9TFqQTS/HKRrJrn5fjppBqwJ28N9qGc6LoSvNUkyBVbKbFJpLWLS0XSXyknTNRdMGYwgQy2FRMlXl7eROmEsCDUBOWCBB0IuoEnYgIDKdSDyZQuiQ06uMQ9wTrh7Eg4d8mMJUE1Eo7tSNe0c2hnpLcEmvBdNkLmXrLsWSEkdyyJSWwqcl+KdEwN60E0ive8cSlgGTMkOb5XEQORmcCcRamMIZOO69aiDOBNM0QlOpjreIlup+Zmv9uFlQpDjUhLkzhgd6bzYKTRbPDK/oXTxaxr1aKVJaplW+BirLppRiAq3T/eGMIIJq0myq7CR8xIGkMncnwdshnwwQqujl0un6kOCHGPCXhLDqECtQe/F5qh9AIU3EATyABwZ4iwSBOSXScI86hAO8s2mvDDkDrc2JSuADCeUbAqMhS4lYIgvVJZgLNzqL69jfLX50H90e+Z3XsiYYIJyMdDPXr+at/7lp85MVPlP5500qSgDofAtHGAbpOZL1QUhiwce9HQHgCdbC03kIln0t0Fub3du48gXRQG1j0tJLo5wlkWMhQG1GkjUmROBIxtu9zr6lenqLh3jW6v9HtLYWH5kqjjZNBYftQXCGhsU9QOzcCkEVxeY5G4i2YOA8FCfWrdvFEpmrUT2YCzc5i/IXhnDh18L7UOdHJZuBmM3LPPOqwSaCzRKRaRpSDTZW8SIDbNpCdANz9XS42xNa3IulEbh2saFKpgXS8YkYxk9UxRABaJaaidFegSi2qMWp3S2fkmeVZbzHpxLWh1ARcJZ9Ds91Fs901qohMlvjGQkrjznOFeKzgIJdhPeRg/P/UzmxRg0dqoOVEKpXx51HxwiTnDXsnE/TWXhpnANh/ehWv/sS9+Ow9JzX3RksQiKrvQuCdQWeWiPbc/JlQxq/KBOpF9VWRzTCMFxzMrw7GSDtTb8kqO5kJ5PJrNOk2o/orUCSQTtDCXnoIkP1sgnlNSUbSPYFyARPIJMESbmJ0HU90KHk51FsddLt+z/lQBPoTJTc0rIwxgaj3NVVyMRd0zwAojM9wbejKALX/OSoNO63iXVb86FSYUMBFe3OxHpGTQAF7s9XpIsPS35PxYlxemHwn1U3ZVMnFZKz7mTHbY5NQkfNT28ATKNx490puiU5aAN9o7quVcHgh3FC0DJPOIrELEJlAsmNqG51Oco5XExpTPZhAusJdwc1K6fggSSAZ36lWAQZjXnQIFajVF1I7hKrGy+E6SS/OCJCZQIaIy4uocygg5uuwIYqug2m8m6i5H1WU/W7S6U8wMW49cAaAQSJtZgYXnQxbezMg8j3vqIZjz8tlUc3nIvLTXvdYyTt4RCDPBMIk0MmlhmKUn35fKhOoY8CMKrrZSLE6YQztZLAe2DRMBuve0no74u0Y9+k514jsOWJ7KfG5zT7dAKn+j/Kz1sy8FceU9UgyuHvM2/EEq4AuRvRyvJvuEcWI++RykkEU/57VhDqARNdPXXE8zgS6OpBznY51kSa9ozMzyNRX5a+XzR8mdU3mkmUrB7MgINJGlUghnyrHgkBCsgQIu5cs1unZYf55iryI6MWitlNvEeVglbyD5YbCBCL4bch7WjNvYc2leK3ws8gV4jCT7/vJ78vNccNAQVE/EutAQDXlLsjObB0jf5Txoou1Vkfqo8ntVzcImQzjG5e+TKD+/0bF435MqlbcpBWt2pUp/txVffx1e8axuNaKVG51FU7h0/HGL+zHaz5zP/74A99NfC6VhSWC9zOrTb4JNKxgC8YZtdIeVstoyQhVrmbiMTGlyE+p1weEHSFMEk5hEqhFljKV1O57xM+qngUTqJp3sNzgQSfVA6OqMEXbHb+vr5pAyc3C93m76V7G0NddPI5chuFxeydky1d1nqJIBQVqFb4eCclP2lpUUdlN8vr6f8bUxz8if/ae9pS+lbiisklX0enSvVEE8/DUsjAMpp03VeIbi7VWh9ZRLCbt1r2TqjQo72QxWYozgfj/h607mJPNoBRswKnFI50xtI4JJDBd8ZKtqA3ZGKoHCs0TKGyW0dHIhCZLYXJjX62kl4NpkjP5XFa+D4PIwaY0suuubzAugg5br7rl1ZioL6K2uhA5rsOYltVH+7jxz39K/lx86pNSZRaDYrwQNl4wkfuoXk46z8iixoi7bcgcrAYbdQGTd1lsbj/63WPB9RKTQIcO4eEn98NrNfCsu78QOQ7wWGui6OCGfZMAeGIgLhEC0tfKspdDxcvhyMKakRxMSHHFZ2UYLSlWUhpR6ObRfNBV8HTQoEQk0URc6vs+mb09KCI+UzG2rVARUJhAlOGVZALRpHhiPRLfWa/vuarIuiPXqEsCORmstzuReTq+L9JZNYgk0HjRwa5qPnGO7nkIRtzh+TrcXEb6yM7FOtyR5sXgnXjWPV/EI4/dh1p9MXK8F4opre1HCTYJdJaI6uZplMmpkoeFesiwobbLFp+1UG8aJUtqgawD0DNfdJAmysZysHjnnZRKqryn0MAPoE14qhzMROa2FJskEzThYCK/7ySXGN13ciXy51QmkOzM1mqHvjTE5BYA6fFBbr+6geA+RXRPoDgyGYZqXtGKa4wae2GswI2GGz0YKWIx21HxcMNeHvh870QoD9Mvavy7ueMoXxSOL61rKf+UxVqVr5jQ1cteMPaDDSHpvVSZQB1aK1vebW4Ag/Lgvk4s8Y00fVPGvS1EMERqEe+FyZmWhgquAzdQDrxziGwKmcCQhqH0yrKYf+dWmvRqdD7cZDU7tARhUTBhmu2eLJEfumYn7n/Fj+HmJ1+G3eMF5J2MnKe6XR4MU8ehkJ822zSZm0i6CslfNpPijzQ7i8kXPU/+mj/wYF9KdqGHJp9360u9PAChhPX4ohi7tBNF8mhupUFsK887pYlqtC6BGWeFTJTcWIt4WiL9fEAk4BvtLqmzSjWfw0qzjW7X7ykjVWUv02UvaEXdlM/BlI2hGhRT5lCxMak3ONsuXlS4RGle8YNX74iwmwR0BrRi3I4XHXISQYXOe6/b9cmdwUSHrZ+54zP41mtvgtPtRI7rkHcycLKMM74lE5DA6pmdxdifvlT+Wtp/f6rMYlBMlUPmvG6j2QvVPH83250uOpp4o+AkN3pCHkYZ6+IzljVyMMq7fP0lE/ByGRyc50wFldHWF3v2oNxcwzdfexNe81+vihwHeDLiK3/8Q5h9Lu9iqjJz1GukPMfLd5Zx7/FlIznYWCEal1Pn3pKXk4XPdrebiG24MXQHp1caqJU9KbONxzYbzQSKehfGCsg9Os4JmDQB4PFy20imqfNj6jUnerkMchnWWw4WK5o2Wl3pH+RmM4mOpDoyQzlgCI4XHFy+o5zYS+msNVST/VrJRa2SnBfJTKDgnXjdh16J/3r77yaO90LBJoEsqFArKU2iHEwE0bKiT2QCCRp+RA5GnExWVPYAYSKX3b6CKiBgLgeL6+Z1KLpZOFkms9cm/iNxE0p69xJ+Ti8PDJEEEt4yi2utyIaEuuEsqhVHEyaQSAIZMjA2ElOKv5EKk449aicdqkkuEL5jYuMUZ+eITcA1F1RlZ7bDSsVBJ0sS79I9x8KFTGwY5XmGMs351aY2MOgFUfkT90UJsHj7dVFlo7GptEwg4nlAuPimtSgVKHm8+464TopkVZWDtTs0X6WSl420sAco5olCDibmDfPN5txqE06Odk7eEYFWwAQitrAG+CaVcl+ZDMOF4wU5fk1keIDwfuIdsSgMp3jHs9TnNzOD3GoY/E2sLfWlZPdq0Uqu/gGYDgLHo8EzoW7qJINopUEag3E5sy6pGGeFTCnvIwBjGc5mYqLkYm6VexBSxkYl7/B3v08CU21HvaPCk0Cdrh9he6QmFtVrjBgUp7MpVKaZToqvvqNONiM9zuKGzcl2yPzfnSJ0oNFhrCBY0em+iloM0CGUMSY3jlL2T5nbZmYwdiY0ea006iSZxSAQ8xNg5gkUSVb3ZALxOeYbB+bx2XtPSnkYnemZ0xpDU95lxhimWBsPzfNYpfIjT6Ul0YLvudRaR9YPEg6x7znvZOU4Hi+4EQ9Syd4kPMdLJoucCdSl7W0AzqRT4w3qtFZys5L9qfOyKbo5dH3g2MI6psquZAKFLdvpFhmDopLPSbWDzndP7B3uOLKIT9x5PCJVE+cA9ISOSddOcY5k26cU1BljCR9XQD+GeSOVkAn05CuncdfRWAMWDatSzMd5J4uLJgo4FouxdQV1wRRtdrqoVTxMBr6Wqhysq/Ef0mLArsmc4Zvc74wihi/q2GKIy8FyhOAlDEJCKQMluM1lM6jkc1iom3VyEK1Hfd/n2n6DSsrSektqXOOtb+MoB0Z8IliiSLR4EOLK5EAnJXsdub6CQvclVoiErwcQso4ScrAsb0W5tN7ChYHUIpKFTqFZCojJbK3ZMapuio2ImPTOtxwM4NXsOZ0nUBAXUyobiVbvpkmgHsyoKwLzwydcPhVpayqvUfPsp4PqfLvry8RRZJExCJQkE6jeJLfmBpQk0Co9CSSkVr7vBwldwvWVlSSQgf+IkGqKZ0m9L8FkEe80zRNIkU1pqoDaz1EM76lJ8TgFut2ld3MTiUMqSwTg85uYp1qdLmmDJZIgK6qkLuW+amVPdoAx6bAozl1ptLG01qIlIgVLNPARSf1+A+r1FacPIt9aR05sXHpQssPkecyHhRr4IWQCiW5NauKhH8Rcc2qZ9h2PKRJtQM8EEt/nsx51AYCokbw4BxiOZH8cU2UPR4Oui14u/RnGk7lA8r7URM1kyY10IQLM1gYAmPrKF8PPJ8iSwiRjW2tmCoRdlcQ9tbu+9EYDNAnJ2VlUvvkNAEDtzm8NxIjRtXjudA3kLQN2CI0bw5PmtkOHUGnWccHSKfzg/V9HBr48fq7BpehibNDWBiDaBEBXaC24OdSbHZxcWsfPvv4r+LW3fANf2T8HgDbWxWdEpOcm7/LsLCaOHJC/jt9/D41NNUCHtUGZQDuqeZxabigFbgLzuOxhrdWRknBqcjvC7NUU02Q83epwOVgpmgTaHCaQg5UgIaxjozhZhlani595/Zdx8ztuw39+60jkz82MoXNBoSXYbxj6MVE63cU9rYDe9gmNNu/omHcyeMwl4zi+tC6JDEAwV8WexwVjBQBc+lgre5hfbcjYmp+jMYZWmKJTJRe5bAbjBSfieUguCA04J3JvwO2RBDKw9LfQQVQbhByMMkkWpVcMvcuPgGiXXfZy5GpZyeVGoyE9kO5nsbTWJleIKvkcWh3e3SLvZMmmhvyeRIttAx8Rj7eGbba7Wnq2DsLXo9P1+8rBeHcu4LIdZRxdXMeplQYunuTJApH4SJeDKV1ITJJAhSgD43wbQwOQnRh834+Mua5mEu+FSJXCcMwDIe03ft7PXn8xfuLaC5F3svB9XrFWk0C6RU1UkQDgqp0VHJyrRxN9BoaGE4GHxBnJBCImgTyR3DKTg4l3jGIMDfCF9Ey9GTEaNklGCjkYtYVtWWmxDNBYGFFjaFqiuuRlE1200uVgoakxwH1LTJORcytNXDheIJ0DCN8IusxNeJyYzBvTZQ93H+eVudDIn3ZfQoZybHGdZPwpWaLrbWQYS39+e/YABw/iv976O/BZJnpcA5VBqYLq+waE77fwIKB23hGJT1FtTv37CSaQPil2x18+HfngPZgqu/B9zuitlT3JEt3IzcugqJVdfPMgN66lvcdhkrVXwnlakcflshnsUJJA11xg5heH2VlM/9VfAr/09/zz938v7JDVI9AvKeOr1e5qvQ4/8MIfkJ5G6j2JZGJkcxt05ao8/XeBnUBt/gRw85/2vQYd4u3Q+ed0zVpe33STcVfQimQC0bwfAQB79oAdPIgv/Otzke12I8fPNUTSFBAt4s0aLwgD+wRLITB/vUvpTHdwjieNyUygfBiDurmM2bs8M4M3zNfxhBe+BQBQaa4BTX489Ts0+J5Vjx7AjI2yo+Kh0e6GsSjh2ctulSsNoziv5HEmEO9qlZwDVPNxzgQScXLUT3QjPYGE+qBX50M3l8GZelMmjO+MsWWo3oWAkBq2FVm9YaGVYK2h9Tvr4QkkkkDTFQ87lWKrSOp3NfYJT3/4Tnzu95+KC8cLeNfXDqLrA2fqTek3qWMdqQUb2QCjGBIFhPcTuVAwwJxYq3i4LVj3Rh3nn2KwxVFwsvByGcyv8o4ilAVbMoGUrLdJW07hCUQ9p+Bm0en6xkbD1XyQiSZWiKqJDRbtGieLrkbXS5clLAfmaZRnL85ZWW/3rG6UvJyUE4l2mRGKNpF2WvSULiQGVQrVlwIYjgrxVNnDequb3JgZMEtUrXiL6D8izgPCZInuGYpNHmMMk0W93EK9RpWyf92ecQBJ4zmAxgTyclmUvRzmV1tks2aAS7vU+yKxMLxQSkr9rGreQdePjkPq9wUAxwNZZJ5YHRVdTk6vNFBwsqREtdqlBqDNUQUnZAJRu7KU3BwyLJSDtbtdssRIzjeNNlkOBoTVYqrcKjJvEN+viZKTYFOSEyZBoHVscc1g8yNMKwlMqoCSne+0UGgH71gfSrbYdKtdkgA93bwXSi5fl48ELBbq2B1XvuMioVuPmKulp10PpkLZy8njqpE8oE9SDwsESwygbYzVZG6vcbijEvVIkl2IBmECzczgwpMh+6TUXEuVJRUVT6BeXc+8XFZuRtQOfwKReCPoylVpcG+X2urCQNIoN5dBwclGYo2WgQfOoBgrOBHvR9K6HLzTTrcTsoAIMotBMF5wsNrkHnMtkxbxypqi+56LLveuu1uRhB+RrDdTtlF04026xkOHsHv5FJ731ffhrz7+usjxcwlRZBUMfZMY+7KAZX3HEZ7MoBQWhDzzTD1UR1AgZOTrra62mFZQ5uNa2UPRzaHgZGWCqmsQYw+KSt5Bu+tjJZgL4nN9yc3JRCKAhG+OCRNbFGdOGthCVAu8K+Z6qyMZRP2eRyWfk/fS7xrzgRxsvt7CZMlLzNmAnp3DGMPeWgluLoOJIOY+o6zrOjN6L5eRiWiRLFLNzU3jm0EwVoh2kR5l2CTQWYIxFpjm8u4qlCpKIVbpNGkDOl50MF8Pgm9iQCySTmJQUyvEvPtWW8rB0iZXlX4LQKu316FWcRVdL82Eml9fGJzRmUBhokpMQPHvbLzg4HjAfrhoglf81YnS1BjaVA4mJkuhnx3EZPJcY6qUNGcDzOitaueCZqdL3nBKBsaq3hMojomA+SLQ1gQ9YiMGAI+/dAoZltQc88+ivSsTJd7BREdj7oVshqHkZo3lYACXClFNMlWjXRMvsbyTRd7J4ITwVSEzgYRsqkmW4ZRj/kik7mARTyB+LO2+MhkmkzKmFSW1JbKJf4vwjWgRO3aJJNpqoy11/ekG+7w4wA15TT2BeKB1dHHdmCVKGoOGlGw3l8FYjP4N6OnmvcAYk4bDAJ3FNq74yxQJY1c2a4gYofa/RiEfnRvAp2uzUVPYUGaMvt6S9VrZw1OunMZb/r/Hyd+BUA5GlXYDAA4dQrHVwER9EcXmGphyvBeE3GC1SXsn1XsSiBTugs/atcylRBctnky9hl5ISHcMkh6DoprPYXmtRfa0BDCwzGIQiHdyMfBHIRtDB/GheM/i8UbB5UmHO48uYrriIZthkjlIjU3ibbZlgoXyDAPW1B9//q345dv/O3H8XGGs4KDV8bHWCs3rAVqHqn2BSfqB0zzBSTWGBiD9bKhztmDoLTfE9xxL2jkqQ4TPS7WKKhUUxdmNi5fDjq76guR40cH9gfnxhWN5+bOALC4S4i+RaDFpbqC+K5IJlCIHiyc7dGw2z+HG0GdWm5goOthRjc7ZQHqX62qMNSvOAaLPQ3RZA8LveaygFLkMY/NBUHSyaHX8vibfowLS28IYewZj7F7G2P2MsZf2+XuPY4x1GGM/c+4ucfhRq/DuKq021fgz1KQDZkwgbirZIFeV1c+TVXYTT6DAMNDJpkvP1CogAPICwLvTmDv8CznN0lqL3GFNNTXtJQdTN3sXTXAJ2Eojaf6XagztDCYHK3s57Krm8d0jiwDoG5iNRGjCF9XJmnQ7EAGu73M5E4l2DqVbmqQkp7Aiik6k2qDTHavj8updVUyWvITmGKC3550sBm22TaQM4MkPUzkYYMaYiRqUGwSp4MkFkRAlM4G8KBOIApEQE+yINP8xgMst1UQ6QJMlVvI5mTgWn02B2oWIkiCQn+c5shpNYzhGDU0p1zhe5GyvlWZbCf6onkA80BKSBgqEoSTZuPamm4ADB7j+7sABgibfTSScqd55AsIrDAi7AaahkndkNyZKAlNId4XxKmfn9X+GajdBAMZJu82E8FYCqEygsBDUaw7NZBje9us34GlX7wDA54uSm415AhHXvGDD/PE3vwifetMLEsd1yGYY8k4GawG7JO09iRe3xDXK7yv4rBd/5T34wDt+D79623+lXkMvxJNAJt2VBkXSGJr4eYbv9MDXJ3y31swaLwh2qUgSxOMNMYcfmq/jgrE8Jkuu4n9l9hkiQdgyeZcHNK01RdxrSteRqRcEK1UkPSixqBpjN9u+cZzXy0uwqMrBgnlp0L3DoAiTQPwa42vfWMGR8/nDd49hfrUZSSSYFBdFEujo4hr5nDHlGYYFdTM5mN4TKIu1Vgdn6k1MFl0p6T21HBo9pzF11eSgPCeFlSYKBOPKvCjUpxsp+4sTNUYZqW8nYywL4HUAngngYQCewxh7WI+/90oAHz/XFznsEJ4bLaJ+W50kAbOuJ8KwqmEgpxGbChFUm3QvEnIwymfFqbFdIn1/suTKKo8JnTaUg7XJLcdl5WatN/1ZrQYLJpBqgEa9xpCBYb7hvGSqiAdO8SoCdQOzkRAmfHFzaOHxRl2gOl1fUrupgVZcDpYujXFlMgHovdD89g9dgV97wl4U3CxqZRenFCM4udBQWSJFzsTwfTOWSCXvyLa0JnIwGSgZyE/VJBB1AR0vhqw46jgUSaAz9ZZRArOSD7uKkJhAbugJZNKlTiS3TQ15S25W/l2KVEh+XiEnxyOp+1Y+WkmlXKPqJ2LSYRGItjA32fwsBf5NG7FJHVd8QAR4t0n6v6G2+qaOw2yGSb+DEuE7ruS5SXZoeJ/OLFFlzIA563AzoXqnUcxyVemUyfu1o5rHyWBD0SF2BwQgN9I7Vs/gwuXT/BhhI11yc7I7WNomNV7cAmIt4oXcsd3EY47eC7fbHngzH5chGHsCDQBhXN+QLeLPf9FJxbi6sTVovBAmgfRFloKSBJoquaiVPSlvMWYCrYUdOwGiKf8msanGY8kVk6SzKOKIJBBtDgiTTiaM7/j3Fd+niKYfALAraNjCVRhRFYFJocAU4hrP9IhTRFEAAK7ZVQGAiDUBlbEMhGx1+TwMkkCLSnzT77xqzNicX2Ny3h4LVCGnVxoYL7qYKLrIZZhsRiHO6zfX643v9cU7sTfbHey/xothh7uwMNbzo84aoW+vTQIBwA0A7vd9f7/v+00A7wZwo+bvvRjA+wGcPIfXtyUwHlDVWh1a1lvSwZUMNrnrSeC8v7jWJAfsgu4vDF5N5GArjTYabdrCq2MCmRjQqhszyoY4lIO1yAknucFa722EOK5IhYQBbCQAlBKo/p+lZWBQO/ZUPHl9VAbGRiJuwicQJhXS/43QP6NpFBx4OS5L6ucJpIIzgTQtUWOn/e6PXIk//4mHAwi6nylMIIqeWkU1n1OCF3oQIt4ZgBZ4CtmUkJ9QGDNCS7/WahuPw7FAYw6YGEOH90SVgwH8WYRJIEqVkhveRyVQtHlDzBn8HLrESGxyTZhA1bwjJQmUubcYJJuWVElNytwm5iy1cyT1vvJOVn5n1OSW6LJowh4ygdq+WSCNbh7HtJLAMNnYisS/GFP9kMkwyQIEaC2shaxB3B/FwPN8QfVOo8hBI13+enTf1GG67A3WHWzAjXTR4x1TaXKwaNIOiHkCncPNfNzEt2XyLAaEkAuJCv0wyM9VqL5bJqx5sVaKdT3OcBJz+EK9hamyF5kvyMnwWEK3TbROkNgENlV8890xKBKESSB+LsVgP2R8tHsar+sQyvf0jO+9QRfXqZIrr4MzRjdPVpuUg+kLyIwBV+3iHQbFvgswa6Qi1nSRHDeR4kXkYH2eR9nLYSXoNivQ0cRFwudpvdXFRNFBJsNtUE4unWUSqEcB5BmP2AUAuCpIpAnfsk7XVyTyGzdP9epQOoqgPMXdAB5Sfj8cHJNgjO0G8JMAXn/uLm3rYKzo8AoskTFTzefgZJmcvEhtdgOIl5G3sKVLyADg+KJo9Uw3hvZ9vgCYmUKKCieNSSEX+bWW0QKlBmctYlJBpSRS5GCTRRclNxuRgwkz1DR5XOg90CH7CAnUlOB7GORgYeIyzgTywRhI5r9jhTD56ftmG7OxgqN0B0vxBCqGzDKA9n3F5SehvIieMO2lE+8HNWFCOU903lqs01hRQKi1X22E45C6fqosEXqHpfD9ocrBAD5/iM20R5LVZrmRZLtjFFyJtqiDtOYWQb+RHCxv5iXEGOMszLU2mSUSzqHNMCFm8H6JTljUpJ2oIFJ9jkxRcLKJKpyJdx4ATN3xLfkz27eP3Lb7x5q8rW/zlo8Ce/emnsfZubxzIkWukvAEJDYaOB+Y/uQt8ufJ5/xM6rPIOxnkMix4v+j+ftMVT1aVjbqDAQNtpItOLvAESv++ejGBIuvQOdrMi+Sq/JyOT0r0nw3E2n5iaR3ZDKHb3yZDSi57GDz3QjbDuxiKODseb5RiRsOX1kLmYNlzQEG826Sp3HozEPdiMVn3cllu0iuYL5RYNO9k4GR5EaPZ6ZKbKIgCba9iXy6bwcd+50n44AufII9xxmhTtmznf28jk0D9JWtjwTq8s5LHlTu5HFkw+gEzPxtRcBKeQJS1WccE6ve+VPK5oGlIuNbqkmkTyn5E/KzO2UD6+qxLAvUai6/86UfhUy95shwTKlEg7Nzb86POGlYOFoXuUcdLZP8A4I983+/7xBhjNzPGbmWM3Xrq1CniJQ4/xgsulhtt1Jsd0gTEGIvQ20wqX+ORJBBtMZSLfJBRpk6SYvGYW2mQNi9x7TyXuaV/juqr0DKopAia65l6iyyPC5lANDlYJsNQjjnod7q0ZI5opby8riS3yG2bzTffGwkvl8VUyZVd0wS6Ps2QGwCmAzaR0N2bMAhUo9h0JpCLrh9qjzuENuBTZU8mmYBQDkZN2o0VHLlgGCUVlAQBxYuhFJODkXyElE2MriVnP6htsqmJj3zQMVH8TEUl74Rt5QnBZlGT3KK2Xl1aaxm1axUQ76WJHEydT0y7b1Glp1HJhLk3gigUlIjfsajM1ZudTWMCmcimMTuLnf/yD+HvBw/y1uFpiaDZWVzyhtcYnTdd4TISETw7Kc/dy2WQYSHVfGg9gWZnMfWi58lfa/ffk/osRAJzWWHaUpIY05UoE8iE8TUIih4fX5RmHmWXS/5UJhDVlN8UOk+gjU7KCE+wY4vrwzcGAYyXQgmOCRMI4M+zl8SrnFeTQK7sBAtEGbp9r01hYAJmCZbNgioVBswZMwU3Kxs2UNZzxljEZ8qk2QCgyP415129q4o9U6GP0niRs9hMfTcHRZwJFP8ssZfZUfWkHFntFmbiNZnLZlDJ56Qcn1JQizKB0gvqYs8WaXyjYaaqjVTEzzsqIRPI9334fn+bASebQdHNao2h488j72Rx+Y5K4jPng+Yrafd1tigFneea1hgaAGf+XKz8fhGAo7G/cz2AdzPGDgD4GQD/zBh7dvwf8n3/Db7vX+/7/vXT09ODXfEQQrb0XqUnZtQgt0vsbAWEbIpTyw0D3XIwuUpttLlGl5LNF6yGJUNj6LEIE4j+ghddLhOaW2mQmUBlqeFWgtTYRnoymHDE/QjKpADZDBV8Ez230pRaYNOOPcBwJIEA4MqdFdx7ItbykpgQA4DpMtdxHzbswAHwxVVsEtKo2iJxIZJGlMpyreyh3uyEZu2GuuNIMseAgRGRgxl4Ap2RnkDp5wgTxdMrDaPOIEA0GakyWtIgghFTJpBIIFPo+EXFYF8EPJRnKLp1DRKwX7mzEnw2/b7URFrJIzJtCrmIMXTa+jCmSC1NPYGA8HsuEJNbYuN4YoneUcwERS+XoGL7RGYpAGBmBlcfvhcAcMFSUHCitO2emUF1cR6A0uUp5bzJwBMw7E7T/xoZYygGnjSAmTxjUzEzg9xqWMWeWlskPUPJtOvBtNVhuuJheb2N9RYtMXO2KAWm8u1OuudOJsNQdnOJFvEbYUw6FkjwxbPbDE8gsT4cWVgbmlhDRcXLwc1mePMVA08ggM9Tx0TRKTamVKnjVNmNrHXU+b3k8oLHacliGz5pp9oxCjA3UC4GjFuAHouKQotRA5AYEygtmQ6ExQ+V+bKRnkAJOVh87xAkLK/eVYGby2C86ESKiyFbifZMxosO1lt0OX5F8WNqkbqD6TsfAlFWtZoEEvHMZEnx6THwLtR5AqW9LzKmX2nKQuZGFgqeeEUNd//VM/CYPRMb9hnDAspI/AaAKxhj+xhjLoBfAPBh9S/4vr/P9/29vu/vBfA+AC/0ff8/z/XFDivEJHt6uUmmxsc721AnZPFZq82OES22ks9JbSld7xy22KR8lqDfLivUWBNGDzc1pfsIMMYCY7gm96YgXmPF4xusVg9D3uv3TuJPn3UN3vgr1wMAynknkgSi+D4ICOM600B/6ravyp/zT3oCWcqwkdg9UYjomwFeAaDGO0JzfyQIykw2j9WCI02o0wyKxeecVD0mUgIDdZEBzLqe8esLN8+mlUoBk86CIuikJFkmSy4yjDP6TO9r6s7bw8++6nLyOBTz1KCyKUqCUCRUVgNvD4BuUL7SaKPe4PMvdT7E7Cyqb/s3AEDhH/6O/CzUzUXBIXruxNhKaRtBaSK52jQO8jE7i6kPvx8AUHz7W0j3Je7pmEFbeRMUnbNkAh06hAuW5/C3t/w9Xv/BV0SOp513/ZG78A//9Sr86WffRDpvsuRGusDQfLqyCSbQRm5eBkJwz//xzj/EP374b5FvNyPHe0Ekc1vEsQuEc/ap5caG+UypKLpZrDa4HIzW9CJaCGqbStaIkHL1IOG0GUwg8ZknlxpDIT2Pg8d53PulYdBQAkDE7Dn+Pdf++0Py532/cRNqX/185DPp1xayiIeRCVT2cshmWEIORo0BLp4MmTd5auG54Ei2PfVdFjKyOQNfwLhfFLDxDBHGgDOrQg4WvcYnXzmNP33WNfi9H70KgM5r0uwaVaNpSgIum+G+hWpTiTQ5GIBoglvDFp8ohbHZjmCuHle68FJlbr2SQGlLplrI7KXgsBgMqU/R9/02gBeBd/26G8B7fd+/kzH2fMbY8zf6ArcCxCJ6asWUCRQs9MTOVsBg0gJxjScC6p5JNh8A5lcbZF26Ws2nejiElNqmebIkMJTs+vRJQXY96+onEzeXwXOfdCkef9kU//tnwwQKNghGgf7sLHb97f8Nr3f/92hShg2GCHYiJnIGFdGCm0XFyw3EBFKTJWmJjx3KhgKgGcruqkZZSsbtwyO+L/QgROirGaN9VibDW6mLhC5FJ57NcPnpvMJWIFUqZ2cx9cbXyV8ZVVKDMHgxMYauKqwoSheSomJ43Qq6/FCCd5F0FvJY0nw4O8vvfWkJAMDOnCE/i9oAkjopByPOG042rDoaeQIF9zV2+hgAwDszR7ov0TVqca1ltCmjopwPmRoCHd9AJhS05/7ZOz6Na4/flzje7zwG4Nl3fQ7FViNyvBfGi07AYjHws/NyEdl0hm1sZXMgBPf8uCN34ca7P5843gtCDtY2SIqpifvmJjCBii5vedw06HwarZZvjIeTiO8E06C9CZ5AYiPY7HSNmJubiZ1jeRxZqGO91TFiK6kszEi8MTuLygtulr9eecfXsOvP/2Sga6vF/KyA4ZJ2ioYGC2uxVurE8Xv5Du5t42QZmcEiNvvU4qy4zrGCoygWKAUd4RfVNL6vQSBYgeJZxuf6opvDc590KXYG8eRUyY00U5HsSMP9HmP0YtVY0eHWGoS9lM70XpeomlRYc9MyCeRirdXBeqtDbttejSeBiMkj0ZiGqz70Cg6LwUAaVb7vf9T3/St937/M9/2XB8de7/t+wgja9/3/z/f9953rCx1miCRGx8C0LiIHI3a2AqKZYZNAqZp35MaRWu0RG9uuD7K5mwgAAQTtPGlG2QCXuJh4AgF8Q3fKsK1ntcCr7E3iZFL2op5A7cBomHR9RSdSpSAF+jMzuOLI98LPb67RpAwbjFrZRbPTTUziJhXs6YqHQ/OrAMxYIpEkUMp500ErUTEuWoT3UnQhuPc43+SbGPgBoZ6dcn0qJr/zTQBc6kIxoQU4C/Cr+7lkhUxb97JYbZgZKGNmBtPzJ6LHiONQyJPMPIHMOqVFmEBtumxCzNfCcJGScMLMDFCvY3yNj49qY5X8LFQmkIkczMQYWnzOqeWGmSdQcF+11QUAwFR9gXRf6j1tREVOyGHV9rrdrk9vCxu07Y6A0rZ7gPOkaX6PrjY6TJVd+fe5v8wQVjUHfIahHMyACVTeZCaQl+NeYkRjc7W4BQxgXk2EqHiLcW/qgTMIVAbmMMrBAODKHRXce3yZM4EMrlHt9BqZp2ZmwOqhV0ux1cDFxw8MdG3TSoeqYWQCAcJAOUw6A/RrFEU1kwRhNZ/DsqEnED/P6em3o4OUuikqgo3sGgVwX0bxLNPmgLFgryHQNkxUifGbz2XJ7LSpkoe51WbYnbHP86gqfpECHU2iqujm8Oqfuxav/rlr5XyhNWtOefTx50Edi5NFF4wBp1aaYXHBMoHOCexTPAeYjCw0dJ2t9B/p+uSqUt7JyADJlEkhEiykTQ+iEhdqkCoCQIBXsSiTXS6bwVjQXUkEPdQJb7zoyCoM9dmLKnsvOVgcpRgTiGI0HF6fi4U1TmMmU60PHUKx1cBff+y1eOP7/0/k+PnE3sDo7oFTq/KYkU8HeBLoQGCUVzIw1zVhAlXzObi5jBwXrXZ6oL+j4sHNZnAskLsZy8GUBIba8asvZmcx9XozE9o4ir/wc6S/X3L5GDZiAh06hEcdvw+/9T/vwj//519HjvfF7CzGP/KfAIDCG//VQEKmtKMmzG1CWlVvto2CTZGgEtJG0nsZ3POv3/oh/Omn34if/84nIsf7ft4H/iO85ht/gvQ8KobG0ABvofvAqRUzT6Dg+n/5W7dg5jP/hl/61kcjx3tBZTdtREVuOvj3E91HqAnnQdt2D3CebOUbMG0p1XIhYwY2Z6M/EAZ8hqEcjB6sh+zNdTSJTR7OBqUg/qLKi+JJoPYGeQKJ5OppWcDYeE8gNY40KWBsJi6eLEjpiQnzUI0bIvO8InV8x3v+FACQgY/Xf/AV+OhvPcno2qZKXugJNIAp/2ZAZWCY+hbV7vo2AKC1tEIuVFULQYzd8Y32KZWCE8rBCNc3rvMT3WBZbcnLyu6sae/moB44AoK1bFIwFSb7FFVFWZMEavcoWP/UYy7CTz3mIuXa+Lp3Ri1yp3kX9ngead9ZLpvBRNHF3EpDmjUPZeFkC8I+xXMAQVUD6ImZksIuMfE6YIzJNvGmLbYFqMkIdTNL/Sw1WGp16cGcaNHd6tBZNgCfJEXVjO51xNk5veRgcajsJiBoOW7QNWq91cVCvUmvpAR0++d8++P4kfu/njh+vnD1BZwtc0/AlgFCKQMV0xUPzSD5ZsYECsdi2vhljGG6HO02k7ZYM8YwVXZxejla0aMGcypdtkRNAs3M4JJ49TGNhRELwIoPHSAljspeDqsNxWiY8qrs2QOn28FLvvQu/Ni9/xM53vf6br4Z+YU5fn2nTxjIpkJ2iTETyICFKQIrIyZQcM9ep43n3vohON1O5HhPzM6CPS+UHVQfvI/0PKp53m1OBDyUROvlOyp48PSqGV1aua/f/MYH4XXakeO9UA7MWgHAJRYVTDBZSjKBqI0GJAZt22143oTSsROgJd+EjxAQFEuGbNMoMcAzrAbSKRNG2lTZQ4bxZ9jYBCZQIfBkXCPKi5JysI35zkRy9fQmMoFUbIS081xg0EYZqn1CZF1WpI5POnC7PPyMxhE87MKq4bXxd9n3/aFlAo0VHJm4MLUm2PmG1wIA1tw8uVAlNvuNFt27FODFNGkMTVi/ZNeo1cEaIgyCspeTiYy0REQ1zgQS6znxEsX4pXarA4KuXcs02ZTY463G7C6A9Hl7XGlGQfUhHA/2XgImrLRwj8jPcYnqFIv+GM4Zf4uhGLSTA4CKR+ugww3D+MJB9c4REAGyycSgJoGoTKBcNiMnCboczFHkYPRAaSqojK63OmTzOQAY60X37YNasGg3iXT1spfDarMjvXDMmEDBhnOpQU8CDSpl2GDsHi+g4uVw7/GwQ5jp2N0RSLUAg2QJQvYGQAsCJ0suzgTBBNX3QZh4AzCT7yH09gEMmECHDuGShWMYX1vCs+/8bOR4T8zM4Nqj98pfi611knynGCSBuiaVqEHGYSAvGlvnXYUm6ksGsikzqavaHYzC9hKQcjATJtCg72TwPAQuXDpNeh6ChSkCYsrz2FHx0O76ctyTaPED3pdoBQ5sDBNIbXUrYCKb3kyIjchpA0aqoMX7vo9Otzt8fkBnAWGibGLWns0wTJa4t0rTwEx2UJSCAoTv09aTpByMzt42gSgmCCYQj6E2Pkz/swu4F976l75MZntsJtROXibm1ap9QiT+Oocx1ljBQafrY7XZkVYBVCb7ZmEswgQK3kvK+J2ZwZVHuKdaJVjTSetXnisP5labZAk0wJMmJh208k4WJTfLu0aJDrwbzJwrBfsBgGaEvNoMG1e0g+QxdXyM3f1dAEDmvu+R38vpiof5Vb6XAvo/R8HGj5re0xIzWhZWyjlTZQ9rrU5EBZNhNCP2qRL3JLVysHML+xTPEYQBXZmYmJkquWi0u6g3uakWeSM9O4vat77GP+tdbycv1qabaAEhc6HLwXiwJKoiVCO5WplT/dZbXbNKj2F3JSCswgpGStoGq5zPodP1pfFnx8QTKAhCTiyuI09lvgwqZdhgMMZw0WQRRxfCDmG+7xsFPBdNFOTPJkmgCUOp0ETJxXw99KaimGtOKC0vpRcL8d4q73uv/Ln89B+mvZcB0+bW1/4S/v4jfxc53hOHDuFD7/g9/Nmn34Cbv/Z+XLxwQh7vh7KXjcjBSPc1yDgMruN5X3s//veX34OfvvMzpOsDgKnPfFz+PPbE7099hpIJFARZpkygUA5GeC8HfScV2cGrbnk1MvAjx3tB+LHNSZPM9HsTa5BIbpES8Gcx14gk0EaYyeqSQBvVlvtskXzuBB+8goN218daqyM3BqOCSj6Hrs+9IpwsfcMjZAzNdhfeRhtDK2sPZT0pa5JAG/GdcdmDI/2iNupzIpidxZ43clnyslcaSJa80YgwgQyYh+Nf+YL8ufj4G8J7OocxljpXDSurb7IYyqyE1z7pOg8dwp6F43jRl9+Nf1BjlLT1KyhiNNpdw+YQajxvUkBubBoTSI1bKXIwAJIN1CGw0iVmZ3HBm/8FALCQr5DfS2HcfIIQB2QyTHZKFKBKGtWGPtS27fEuvCZF5FqFf89WDnZuYZ/iOYKo4FDZOdJMcoXTGKnUTNx8M6bmjvPPOn2cvFibGOuqECZgJp23ltfDzaZj1Eq9ifW2WfcH9b6o1cPpoGJ+Mpgk0+5NMDuWG6IdIn2hEdd3bHHNbLM0qJRhg1FTDE0Bw7bNAK65M5S3lZ74eHKgueOLn5Y/s337Us+bCDokAEGHFQKTbVyjmyd9zzHJT+2+O2nvZVCNzPldyE9Jq0YGCaLfuPXD+JPPvSVMKqTId0puLmIMTa5im47D4DoqzTX8wRffEbaVJsimai95sfx1/L67U58hN0sE6o12IAcj+jcVHGQzTHaCI7+Xg7yTiuzgZ+74TOJ4v2sEuNkwtXOcMNc9usDvixxsDjjXiIKHaop+rhAGzwpNvUuUMW4yhJmvkBdSNlcVxYvBpNvkVoCIGebrLaNAfTqQMWyGMXRJiYEo8UY176DZ6WK91ZEFro1ipU0pLcdb3e6GMI4imJnBNQ/dAwD4YSE/H4JGFCqmlSQQOX6dncXYK0JPxeL++6NryjmKsWQSqN7aFCnjINhRzQcdDDtmHXiDbom//8V34oce+EbkeD+ocXnRoRf7VB9S6rosYtLN8gRSmd4UY2ggLGYYNQGYmcGjDt4JAHjMEf5+Ut5LwbY/KtajlPmDM5sGYAIVhByM7sc0JZsoBMVWA7nrVMnF3ErTysHOMYZvttqiENUJtdNCP6iTQ6dL7xqFeh27lk8DAJxOh7xYVyOTsgk9k0941OSWCJbqjXQqooqpkofFtRZW1ttmdF+F4URlYV02zVte3nl0KbjGdDkYAOnhZDJxSQf99fbQtl81AW95GSaBugYJMczO4sK/+GP56+T3iMmS2VnseOlLwt8JFZGJYui50SR2qRsvOlhQFmuAuJGOSX7I3dwGqUYOSGMvBXIwIybQIDgL2VR5cU7+mvW7qc8wk2EoOlnOBDIwk81mGPZOFXEsCJKo88ZAGPB5CAbm/GqTfF+ile9dYl7byMTC7Cycb98OAKj849+dc9ZA3snAzWaScrAhTJa4Oc7eCINuSkfMsEI8rOyBQSFihTOrTSNpxg7BBNoEOVjRMAkUTdrxYxvVhUh4XwCbxAQ6dAgXLZ3Cp970fPzB598WOT4smLrlg/LnyV//Zdp8MzOD8YVT8td8u7EhyS01lm916C3RNxNq9z2jGGDg9UvZbwzIBKIWg6fKHk4vNzfNj0mVt6XFlYkkkInn6aFDuHjxBD78tt/Bqz7695Hj/SCYQA/N85g0bd9R8aIsx65Pk2gV3SycLIvIwdLWvjgzyiSZPl3xsNxoS9aSlYOdG9ineI7ws9dfhEfuHsMNeydJf1++DOstervR4OV/9p2fw2MP34WnPHhb5Hjfz7vta/LnzKXpTAoAwOwsqv/D6bSV97yL2NUm2LzU6eZuAHDhOM9e33dyxYgJNPGl0Eul+os/T7pGYXD83SOL/BpTJnKZBAomHzJzC1GvmGHtvGGCmlKlBHhCjJxPmJnBjtNH5a85wkZfnDc5Z9aqfKLo8g41nS7aHZ8UmAkmULfrSw03tYsWAHz6jc/D+9/x+4njfWFajRyQxl4Oqj1tA5+OgXAWsikG4Be/9d/4za9/IHK8H4TBq2kr2n21kvzZxFvNGAN3WArlYNRNxXTFQ9HNYv9p3r1vw4KkgJFaXOHJpurxI+dcPsIYQ7WQi8rB/OGUgwG8+nrMgIFVjaz/G+Mvc74gxu7plYaR0fB0QPevNzsbXjBRJR2UolOYBFI3PBvPBNoUT6CA1XH53GG+JseOn3fMzqL4AoVpu/9e2nxz6BB2Lc/hqQ/cip+84zMh2/YcJ7fUd7m1CQnMQSBkOPOrTRmzkQrPA65fO7/4Kflz4S9fRl4b1GI1dR/A/VUbMmbbeBZhOHekMcyrWiYQcd4I3r9HHb8f48KPSTneCyIJdChIAqU9R1EgFKCylRhjvPtxvaXEy/3vTawNy0pTJKofnmARHVuky64t0mGf4jnCz15/Mf7rxU/EIy8aI/39qpIRbXWInW2Cl//q0wfx/tk/xKOO3x853hOzsxh/rZJJpmhLg0B/7AyvpFRPHyMtvBWlgg3Qq9FX7+IdGQ7N1+ma79lZ1F4WMkuqBx+gaWbLHiaKfMOfy7DUSUgwBcJubvRNdNTQcOsngabKHupNxdjNpDp/6BAK7QYm64u49uj3IsfTzsvAR23lDK45sZ903kQppKq2OrQ2u2NFF77PF6j2AB2WLps/gscevSdx/JxjABp7yQt8OgLT9o0w8z2b6xPP6hWfeB1mPvvmxPFeKHm81XPbQA4GhJ3IvFyGbJQ/MAbpsBQwMOdWG+TNJmMMtbIn594Nm28C5tujg7E+sUY3/zZBvLOKCQNzsyGkTABtzRNMr6W1tplEYAtAxAA8CWTQ2rjsodXx0Wx3pen7RkFlbFPiDdHwY3m9bSYVHgDTsQYFG84EGtJGFBIzM2D1OnYv8kLQ9OoZ2nyzZw9yfhdvfd9f4O9veXXk+LmEyvZoGjBSNxOqia+xB5np+jU7i0t/53ny15JBkaCaN0vOAsB00OhFqA9MPKMGgZpALqXMU3EmkJEn0IDvpWiwIZhAaYn4kpfFavDsxDVSl6NqPoelNXpiXMQ1Ig7t+vSxKGK244u82GLlYOcGwzdbbRPEKaSk7ltnIbW49NgD0WNpi2gQ6O9c5hKNbJfG2hDBkuhQQ5WDXbGzLNsmelQ52MwManPHws9u1EnXyBjDrjFuUEwxJ44zgUyMofNOFhVv4wxUNxtxY7eubyAtCoKvL73+1/Hed/1h4njaeV/951/FR972O6TzhGndmXqT3D5capzXwnajpPE77EE0QgrzmXoLbi4zdN1LBn2GxcDrqNnukucaAKgd4B1PyovzQ9kNRxQJ1ltmmwq1w9qGtXsOkq8v/Op/4I3v/z/44fu/Fjl+rjBWcGSwCAQJ52EbtwFE9RWgzfMJJtCQJrcGQVUmgZpG0m71GZp0FBoEKvuPIgdV5WDSgHajPIFKLpbW22i0g25TG80SG9JGFBLBvPLuf/8TvPtdL8VYYzVyvCc2aV2OF3SHkQk0VghNfDd8vpmZgbMSdpDdd+boQLYV1GTOVNlD1w8ZIiZzziBQ9wxpUjeZ9BjEE2jA99LLZTFedND1+VqUFuuVPQfLKhPIgH1YcLNYb3WkT09aQkeVQQOBHIzcQTrKBBrGZOtWhH2K5wkJCillQJ+F1GL30in8wIFv43lffV/keL9zAODH7v0SLpt7CE84+O30czC4HCz/3ndj7yI3vC5/5pO0TdmhQyi2Gnjy/tvw6CP3YKq+QLpGINwsUaQgcuISTCCf3joc4K72wGgkgSaLIa0YMJSDBUFZsdWA1wkWHUpQphgoZwVdPeW8mmi1u9Igtw8fU4zuqIsagOEPohGO87mVxlB6Fgz6DEsuZwIZeTHMzuLC93Dvi2Y2N5TdcMpuTr5XJt9XpIvORs03QfK12GrgR+7/evgun+MKezXvxLqDmc27mwk1gUE1GgYCTyCqHHyLoDKArwcQfYYbzgS65cPhzz91I4HhLJhA9HbIg0K8w/Orzc3rHDekjSgAyHnl4sUT+P6H7kgc74lNWpcrHp+rF9e4MfQwbk4FE2gxYAJtaDI9iL//6HNvwbVHv4erTz4YOd4P1c+HzRPyj3o4aU0WDJEjCwHzZYPjbJWtlDZPnZUnEDDweyk8oCgJsbIX7Q5m4r3n5bJYb3eUObH/5xXdLLIZFjKBDDp+1mKNL4bxPduKsE/xPKEUvAxn6i10fQN944BSCwbgXe+ZwR9//q2R4/3OAYBHnngAn37TC3DD4TvTz0EYLInOTKT7CqRn1xzl1fnaKSJ9NLiWt//Hn+OD7/x9ON0O6RqBMOCkGHmLbP7yuqB0mgXtIuE0Cp5AInmpanrJi9qgQdkA5+2oqkaIdGNoIKBMSznYBr6Xm4iq9OloDmWlEsBAz7Do5bDaDNpsUyvmMzO4+giXI157nM85w9YNJ5NhkkFo4j1SU5JAG8YE2qQK+1ghmgTqbvTm5SwwbZh8E0nZpUBeNEpMoEpE0kFf83Z87hPy59IfvmTjkrKzs6j+1gvlr9UD96fGGyoTaKM9gUS8cHq5ic5meAINO85mvtmEdTmTYajmnYAJ1IU7hP5eY7FOThs63wTx9wu+9n586B0vQb7TihzvidlZjP2fl8lfvf00ewfxvoQeOBv7vtQiCf/+n+Xlssg7GSzUFSbQJoyPaYPCc9ITiL6/yTsZrLe6aEnmfP/zGGOygzRgJncNk31rwWdv/f3UMGCbry7nD4wxVPM5zAXab5IcbFAMsogOuPCGnkB80jPprnTZ3GH+MS1iF4ezCA72HeF+Srlv3ZYqBxFyMNGu2Kgt+uwsal/k1Y38m94wVGyDQaDT9BqZtQ4alBmeN13mRuOnlhtodWgL72RJsJwaihxs+AK6QSCCwNMrjY31A9pkFJ0s6o22mRfDoUN49NF78ff/9Sr8zX+/JnJ8mCASroPIwdzsBkr+NqnCPhbzBDLyH9tkiKQzQEv2550s3FwGS+sDeHQMOQpOVn5P5A3Z7CymX/Ji+Wvx6EMbx86bmUF+eVH+Wl1fSY03Qjbw5jGBTq82Nm3TONTYAkxbkbBuDmmLeCebQcXLySSQiXTaGGdhW1FdOC1/zcAn7QP2TfMmD3ceWQJjZszZQVArhXM9ZY2dKnlSGbEpHl/gnRYBWqKk7OWk1QVgZtacz2XRUJhAND+8cF03+ayCm0XJzeLEEt8zb3Syb7vAPsXziLGCI71VNnTiGmQRHXDhFcHS/GqQ3KLcV7D5esqDt2FqdQGPJ0rPBg4OZmdx5Zv/iV9noZoqB8llMyh7OYXCSAwAA4bTjpM8uZWfPzV0shNTxDW9xkmgTUK1kIOby+DUSgPNdpdkUCoqPKeXm1IOltY5bqtATQINY5A6KMp5HsC0u7QOcAAkM/In7/ocLlo6FTk+TBDvmhHr8O7vAACane7Geh1tQoW9Wshhab0N3+fvYtcgYNxsqEwgquyXB8OjxwRijMn5hmwMPTODyuKc/NXER8QYQVzxS9+8BVedOoBycy1yXIdyxBPIoMX2AKgpvnttA//BkcawM22DToamXSo3E2NFBwt1LjHc0JjtLGwrJtaWcPnpQ/j/bv1w5Hg/TJc9zi5ptOFtgt/h1CdvCX8hrLFTZTfs9tf1UyVT5wKCCUTxOyt7OTTaXdnhi3sC0T1P11tdo266Yl0HzAs7Ebn7Rjf02CYYztlqm6BacHDasIvWwBhkER3gnLL0HuH3RZIkBJuvxx65B7f90y/hqQ9+M3L8XF8jZmZw+VHOBNoVGF+nVwJzipkZMTALGE57Frh5dTuTHTrZiSlULysgMIYewiCVMYbpsodTSw002h1aG2AvBzeb4RXYzmgygcidCLcIxoMKLLUDHIAtYeQNhKxK8vc1O4upN/9r+PsQeh2ZYKzgoNP1ZZWSJ5zP80X1wPTnPyl/LhC9LHgwzKWnwziHng1EV0xytTbY6D3vq+/D9YfvxL75o5Hj5xRBXPF/P/kv+PibXxT6zPWJN7IZhpKb3ZTuYEL2cHqFM4GcERsbowjJBBriJNB40Qmk7pvgQTagbYXT7eBT//ZC/MWn3xA53g8i1gM2QSI0O4up33pB+DthjZ0qubLb36Y8ewC77+f2Hc1v3p6aqBJG10ISZpKY8XIZrLc6RhLZiudIaw0TY2ggynQe1oLQVsNwzlbbBNW8o8jBRuOrEMGSmPRIlcDN3pQdOoRL54/gTz77b3jNh/82crwXqkqnmg418RH8ezfe9Xn82q0fws9955OpnzPsKLlZZFjUE2hY5+LpiodTKw00WjQmEG+x7XIm0Ab7Pmw21K4bQ2kMPSDGiw7qzQ5WGx0z/6YhlxcA4XeW1oFEYmYGtTMnose2cNJ5ImZCP7TdwWZnMf0Hvy1/pXpZCFr8qDGBgLCTS1oLZYlgo/fHn38r3jf7R6TEzMAYWOruYKVBb4c8KIoulwrOrzbh++lmqxbnHxNFF2fqLay3OkPbAGS84GKh3kSzM5yStbPZBxjPN4NiZgbeylL0WMoaO1F0N9cTaHYWV//TKwEAR8Z2pCaqkt2P6Uwgz8mi0e5KD02SHKyQk9Yapj5/gglE7iBtkQr7JM8jVDnYqMhOAB4snRYyN8pis9mbskAOcvPXP4jdyzQ5iKDuAwYt4oN/r1ZfxJ9/+o3Ys3gi9XOGHdzYzYnKwYZ0AzNd8XBquYFGu0teNKbKHuYUJtCovJd5JytZeUMZAA6IsWLYBc6oAjvk8gLMzqJ6y4cAAJUvfo7cLXHPAu+w+HPf+UTk+FaElGcGBYVOZ0jnmpkZjJ0J1xGqlwUvLLTJxvVbBrOzqH2Wj7/Ku95OG7ubWQgaMN4QhqZSDrZB3xljDOMFB6eX+bgflULEKKNW9nB6uYHVRgdFbziTQGMBE6g1rEmgs7B3EL6blQfv21jmq7KWPu6hO7XH46gWoh44G84EmpnBNQ/dAwBY9rhfUr/1qBRLArUNZNdFl3sytqUnEEEOllcK6qZMoOP8OedPn9xYufs2wganTS36oVrIYa3FO1ptqDH0JqOSz0mnfnKHmptu2ryN2MtfzjPj9Xp4LCXgrBZyOLqwDiBoVUzJXg/wOVsBqqa3O6zVeXBzvFsPzKPZ6ZLHYa3s4vRKM6xsjFAAPlZwcHLZMFky5BhXGE4jY3gdeIlVH38TsA+onjkF3Px/+Z/1myP37MGFBw/i1tfehPG15cjxrQhB8T+1zAsKra4/nN/xoUNgAH73i+9Eod2IHO+Haj6Hw/N1VArO6DCBgrE7/oRfBS4BqqeO8TUQSPcgBPhG5dAhPmZf/vKNiwkGiDdEEqizwZ5AAGc4ngqSnyMzNkYYtbKL5UYbXd+nMzc3GeMFB4v1wLx6WGMA0/cymG+mnvCrwMWPRnX5DHDzS8J/61xjzx7g4EHc+6pnIxs0D5HHe2Cs4GC5weeNTZH+HjqEMd/HT97xGTz9e1+JHNdBdsUN2EodA0+gyZKL1WYHKw16I6B4Uoz8PGZnUfvYp4Ebfhpupx0ynIDhK+JtIQzpTLA9UI1sYEbnq5goumi0+QQ5KhWHaPaaqOvdIrITUyTc/Yc0CbSzmseZYGGjGpROlT2cXmnIlpej9F4KX6BR6qogAhhghL6rwEtsd2Ba3crmjLol1uqLyAk5zRZOOotufQtBZxXupzCE33GwAfjtL78bN3/9g4njvVDJO0GL+M3xidgUBGM33w48AdtNuiRxyNl5E0UX86tN2TlyIzdz48XQTHZkxsYI4+JJzmJbbXZQ3GhJ0oAQnkDrrSFlAg2CYL6ZqvNuf+TOwoMiWGO9Tpu8xop93vJ6a3NYn8G68/e3vBrPuO8rieNxCBPpUwHzkHsC0a5RFGpExy7KXFXJ57Da7KDd6ZoZQ8/M4OLTvMlOIxvEfVtY7j4sGJGZYGtCdH8BRmgDg1CfCxgwgTYbhgHnwNnrIQ9sB4GaEOt2gWHclwHAlTsr8mc6E8jD3EoTrTZd47xVIJJABWc4g9RBMF4I55qRmUODit21R78HIOiUpBzviRFLOssKpTDk7wxpu+wBpUwjaQwdjNHL5x4CoDRe2KKSRBW1oEAgCAAbuTaMFxzMrdI3VhbnF2qsMaxMoOmyh07Xx4ml9dFJAgXzipBB71idjxw/5xhgjRVx12Lg/7bha5jheiTayZ8USSADyZrY6x1f5CqJHCEGE/velYZhZ8xDh3DtMR4T/fADX48ctxgco7Mb2IIYU5hAw2omNwhqShs/b0Tuqxq0oOx2/aHtiLVZqBZyOHCaS9y6vj+0vjmXTpfkz1RPoFrZRbPTxemVBnIZRlrUtgrGTI2GtwCiTKAReScDyvn1R+7CJ9/0AuwVSSBqt8QtmvSJo+Bk4WYzkqbe6g5p550BpUzVvINmu4t6szOcya1BEIzdn/vOJ/HYI3fjsrnD4fEtjqmyi7lVbqwLANkN/M7Gi6GvYnYYx7xFBDs/8REAYwCA4t++Ejjxg0M3D09X8gCAw2fWsGssf56v5hwhmG+efdfncNWpA7hs/kh4fKNguMaqSaD2ZngCGa5HYwUHbjaDk8s8kWPiCST2ejIJRDKGDjoMrwVJIKqSYM8eXHXwID71xufjosUTkeMWg8OuLucRqhysNKRmcoNAZQINrfbYENWCA98HVppteov4EUU1H23xOKxysB0f+y/5c/kPXkIykROL2uEz9Y1vN7rJEMHIKM01gsoMhAaHWx5BJY8BuGLuITjdzpaWdQ0KxhjGig4W14QcbBMC6EExAONTrP9nVpuj0wEqGLsZ+Lhi7iFukj0iY3ey5KLT9eV43FhPIFd6D3kjEkONLGZnMf7Cm+WvOx+idQfcbIi1cq3VGZm4XMw3Wb+LR5zczz3Zhmy+iSSBNov1abAeMcZkExWAd+wyZQIdW+JJIEqRppLncdrSesusRXzwXV8+fxj5Dt9/DNt3vRUxIjPB1kSUCTQiGxiEbfyA0WnlJyiMS2stLoEa0sTHZkB4WQBAe1ir87OzGHvhb8pfr7r7NlJgJha1IwtrI+WdA4SbzlGaa/JOVjKAVAbilsaIybrOBuMFBwv1FnzfD9rrjs47WQ2C4eVGG6NCBBrlsSvmz/nVwAR1Qz2BwthwWLtNWQSYmUGmvopKYxUA8PATDwylV8lkKRxTIyMH2wLzTZQJNJy+drWyiw988wjuOb7Ei9zE/Y2IuY4urAGg2S7IvdS6Yae6LfBdb0UM32jcRpgqhYyZUZJoTH/za/LnytVXDF1FZBBUC0H2eq2N5rC22NwkVAs5rDTaaHe6w1udn5kBU7qyXTZ/mBSYTZX4onbkzBrZTHpLYHYWk//6WgBA7k1vHIl3EgAwO4tsg1ewpl40fNXXgTGCXmKDYCxIAokWtM4wzjUDQmUCjxTrcETHrti8CKPyjazoj2psOJIIPEk+8tbfxgff/hJcPn84cnxYMFFUGPqjFL8O+Xyz6Z5AA+AXv49Lqu48soRWxyd3q847WZS9nGQRUcaVupdqd3yzIvKQf9dbESM0E2w9qLKpkVnoZ2ex81UhPY+JNn5bfHOmZq95i83hm8g3C6qxW6szpEygIAB71j1fxKVzh7mkRjneC7UKfydXm53RYQIFbVTZmQUAgL+8NBLvpLiv9Rz/znY9cPdo3JeFhOho0+4ERu3DONcMCLUxxEglgUYUYjM3txp07drAGEAUI4DRYm6OJAJPkksWjuPRgXGtenxYoCoP8qNU4BpyjCkeOEbyp03Esx51IQDg9Eoj2N/Q19maYSOgOBNoKIvI2wijE1FtQUwq1Z7CqCSBZmZw5WG+EOZbXCc6jNRYU4RmZoYUxhFENbaoDWNlQwRgr/vQK/GZNz0/cbwXJosuBBN2ZDZmQRvVYotTdq88dXAk3klxX/sCM8hLiWwvi62DsYKLhXoTraAl08iYfyOUgwGjI5seZYgK9pkgCbSRkvCRLBCOKgbsDrjZyGUzMiEhxrLFxiPvZOBkmfQEGsakR8nNouBkcWIpSAIZ7G+E/UeGmXUHW17nqgpnG++lhgF2JjiPUOUm5VExNT10CCXfx7988BVhu8bg+FZGmL1uo9keUvbLJqGqGrt1/KHUOOPlL+esEEUSRgnMctkMJoou5lebo5MECt69X/nmLaitLuIn7v5C5PiWRXD973r3n+CByYvgddqR4xZbH1Nl/i5KJtAQBtCDomor81sKIgYQXbs2MgbY99n/huw29axnAH/0Yit9GFYM2B1w0zE7i/LpLhZLkxh73T8CC08evmscQTDGMFZwwu5gQ7h3YIzhsh0l3HtiKbC7oK9HgglEtU8oi/1DkBQbGZPyLQr79M8znvWoC/C0q6bBRsVoOGBaPPN7X8Zjj9yTOL5VISooC/Um2l1DHeuIIc6KGsrq/FmYyO2s8vaphVFJAgXvntPt4Ma7P8879ijHtyyC679geQ5PPPjtxHGLrY9a2UWj3ZU+LMMYQA8KKwfbWhgrCjkY97+gSB8GwuwsxpWmBuMP3GtlrsOOYfcqCaTT2SYfu9XjR+yY2kSMFzmjtdMdXvnTlTsqOHC6biwHE0wgKps1m2GoeDkrBxsSjE5EtUXxul98DN7yazec78s4d9gi1FhTiOy18APYznKwsMXjEMvBgIEDsyt3lgGE97nlMaLv5Mjel4WE8EY5tiha0A7pXDMAVM+xkfEfG2GU3RwY474ZwAYygQKZq0CtvmBlrhZnh2BM7Vk8DgCYXj1jx9QmYqrk4vRKA+2OP7SdhaervE18o90x2t+IDmEmyZxKPofl9XZgQm3XvvMJ+/Qtzi1GtI2fyF7PBQHgdqYwJo3dRuhZzM7iotk3AwDKH/7gaFTKRvSdHNn7spDYWysBAO45vgwAIzXXsHe9S/6c/7OZ0ZhrRhiZIAY4vRzIwTZq8yK6Tb3lt/CW//hzsNhxCwtjBGPn1R/5O7z9PX+GH77/a5HjFhuLWsXD3EoTjU53aP3fdlTyaHa6OL3SNGI51u7mLOyVM8vA3r2kdaxacEJ/1W28lxoG2Kdvce4x7NTYAVEtONIPYDszgVQ5GG/xOJyVDWMElOnasYMAgO7q6uhQpkf0nRzZ+7IAAFy1qwIA+O7hBQAbuPHebIiOfT43vK4ePTQ6c80IY6zoYK3FO01u2OYlkLM+4uR+PG3/bYnjFhbGCMbOjtUFPPnAt8JuqXZMbQpqJRengs5bw+r/pnb5Iu9vZmdRe9O/AAAaOQcgdoOu5h0rBxsSjEhEZWGx8ajkcxtPBd8CqHicFs/lYN3R8ekIKNOT9UUAgNtpW8q0hcV5RNnL4eLJAr710AIAoDgq3jnBXDO9cgYAcMXcQ3au2QJQfZw2LAlkZa4W5xp2TJ1XVPIOltd544ph9X8bLypJIOrcNjOD2pmTAICrTvHiKWUdqxZyvLuwlYOdd4yI6YWFxcZjouhi/+kVANubCZTJMJS9XEDn9OGMSiY/oEY/5cFv4te/8Z/45W99NHLcwsJi83HxRBFffmAOwAi1yw7mlL+/5dX4yp5H4vLTD0WOWwwnIkmgjYoBtkq3KYutAzumzitUf8lh9X+bLA7ABDp0CNdmsvjNr38AP373FyPH+6GSd7C0vsxbxI/K/mGLwiaBLCyIqFU8fGU/34yMjARqQFTzzuh17NmzBzh4EOPrK3jZZ94UPW5hYXFeILqPAEBhVJJAwVzzhIPfxhNsZ7stA9ElFNjgGOCmm+wG3eLcwo6p84ZyJAk0nGvYeDGc28idcffsgXfwIGY+++bE8X6o5nM4EzTZ2c6qimGAffoWFkRMlcJMedHd3vnTasGRndKGtjuYKSxl2sJi6DCS866da7YkqgU+/jJshIofFhYWG4qKwiAcVibQhLrOesQk0IDrWLXgYLXJfamGNSm2XTCco9HCYgihGqeVRqUiPSC4P1Jgkj0qwbDtNmVhMXTYvf9u+XPxh54yGubJdq7ZkhBMIFu9trCwoGLX174gf86/+EVDuYape5oStdgy4Dq2FeRx2wX26VtYEFEbRVnCgKjmHZxcWgcwYpl8223KwmJ4MDuLq1//Kvlref/9o9NFy841Ww7CEyjDRoT9amFhsbGYncVVL/0t+Wvh6ENDuYYxZU4z8t4bYB1TvdW8Udo/bEHYJJCFBRGqN8XIyBIGRK3sSjnYSCWBLCwshgczM7jq8L3y1/H1ZdtFy+K8oRowgUZGAm1hYbGxmJnB2JlT8tdL548M5xqmJKVKv/6rG5qkUvdSdv9wfmGTQBYWRFzw5c/Kn0dGljAgphRpnKVzWlhYbAgOHcL06gJ++L6v4blf/yCYctzCYrMx/q2vAwCyZ+aBvXu3dQxgYWFBQLBWvejL78aTHvwmLlo8GTk+FJid5eykAMWHDmwoWymyf9jGnZaHAfbpW1hQMDuLy1/yfPlr6cEHhpLSuVlQpXH5nM3kW1hYbAD27AED8KYP/BX+9LP/FjluYbGpmJ1F7TV/F/5+8OC2jgEsLCwICNaq3//iO/GO974MGfiR40OBmRnOTgqw78zRDWUr1UqWCTQssEkgCwsKZmaQX16Uv06sLQ0npXOTYOmcFhYWGw7bRctiWDAzg7GF0wCAsfUVfmwbxwAWFhYEbIU1LGAl/dOH/gbPuvsLGBfz2waxlWoVVUlg9w/nE6QkEGPsGYyxexlj9zPGXqr58xsZY99hjN3OGLuVMfbEc3+pFhbnEcFk+Lyvvg8//d1PbXtZQs3KwSwsLDYatouWxbDg0CFceuYIdi+ewP/9xD9HjltYWFhosRXWsICV9OP3fAmv+/D/Sxw/1yj+x3vkz6Wf/F+WTXkekepuyxjLAngdgB8BcBjANxhjH/Z9/y7lr30awId93/cZY48C8F4AV2/EBVtYnBfs2QMcPIg//vxbk8e3IWqWCWRhYbEZuOmm4QqYLbYn9uxB8eBB/M/rfyNx3MLCwqInhn0Ne/nLubRVkYRtGFtJ+A+9+L0AgNoDd4d+RMP8jEYUlBL+DQDu931/v+/7TQDvBnCj+hd831/xfT8QOqIECNGjhcWIYCtQOjcRahKo5G3vTmkWFhYWFiMOGwNYWFiMIjaTrRTzH5qob29rjfMNShJoN4CHlN8PB8ciYIz9JGPsHgC3APj1c3N5FhZDgq1A6dxEjH/wP+TPlSd8n6VzWlhYWFiMLmwMYGFhMaq46SbgwAGg2+X/36h5LZDP3njn5wAAbrcdOW6xuWAhgafHX2DsZwE83ff95wa//zKAG3zff3GPv/9kAC/zff+HNX92M4CbAWDPnj2PPXjw4FlevoWFxaYjoHPuDeic977q2fA81wbEFhYWFhYWFhYWFhZJ7N0LHDyILhjamWyYBLrkEp58sjjnYIzd5vv+9bo/ozCBDgO4WPn9IgBHe/1l3/e/AOAyxlhN82dv8H3/et/3r5+eniZ8tIWFxdAhRuf0Om1L57SwsLCwsLCwsLCw0COQ1WbghwkgK6s9b6Akgb4B4ArG2D7GmAvgFwB8WP0LjLHLGWMs+PkxAFwAc+f6Yi0sLIYAAW3zcQ/dqT1uYWFhYWFhYWFhYWEhYWW1Q4VUR1ff99uMsRcB+DiALIA3+75/J2Ps+cGfvx7ATwP4FcZYC8AagJ/303RmFhYWWxNBp7R3vmcGjawbPW5hYWFhYWFhYWFhYRHHsHdL20ZI9QTaKFx//fX+rbfeel4+28LC4iwgWjzG20nabL6FhYWFhYWFhYWFhcV5x9l6AllYWFiEsHROCwsLCwsLCwsLCwuLLYlUOZiFhYVFApbOaWFhYWFhYWFhYWFhseVgmUAWFhYWFhYWFhYWFhYWFhYW2wA2CWRhYWFhYWFhYWFhYWFhYWGxDWCTQBYWFhYWFhYWFhYWFhYWFhbbADYJZGFhYWFhYWFhYWFhYWFhYbENYJNAFhYWFhYWFhYWFhYWFhYWFtsANglkYWFhYWFhYWFhYWFhYWFhsQ1gk0AWFhYWFhYWFhYWFhYWFhYW2wA2CWRhYWFhYWFhYWFhYWFhYWGxDcB83z8/H8zYKQAHz8uHn3vUAJw+3xdhMbSw48MiDXaMWPSDHR8WabBjxKIf7Piw6Ac7PizSYMfI1sQlvu9P6/7gvCWBRgmMsVt937/+fF+HxXDCjg+LNNgxYtEPdnxYpMGOEYt+sOPDoh/s+LBIgx0jowcrB7OwsLCwsLCwsLCwsLCwsLDYBrBJIAsLCwsLCwsLCwsLCwsLC4ttAJsEOjd4w/m+AIuhhh0fFmmwY8SiH+z4sEiDHSMW/WDHh0U/2PFhkQY7RkYM1hPIwsLCwsLCwsLCwsLCwsLCYhvAMoEsLCwsLCwsLCwsLCwsLCwstgFsEugswBh7BmPsXsbY/Yyxl57v67E4f2CMHWCMfZcxdjtj7Nbg2CRj7JOMsfuC/08of/+Pg3FzL2Ps6efvyi02AoyxNzPGTjLG7lCOGY8Hxthjg3F1P2PsNYwxttn3YrEx6DFG/oIxdiSYR25njP2Y8md2jGwjMMYuZox9ljF2N2PsTsbYbwfH7Txi0W982DnEAoyxPGPs64yxbwfj4y+D43b+sADQd4zYOWSbwCaBBgRjLAvgdQCeCeBhAJ7DGHvY+b0qi/OMp/m+f53SQvGlAD7t+/4VAD4d/I5gnPwCgIcDeAaAfw7Gk8Xo4K3g362KQcbDvwC4GcAVwX/xf9Ni6+Kt0H+ffx/MI9f5vv9RwI6RbYo2gN/zff8aAN8P4H8H48DOIxZA7/EB2DnEAmgA+EHf968FcB2AZzDGvh92/rAI0WuMAHYO2RawSaDBcQOA+33f3+/7fhPAuwHceJ6vyWK4cCOAtwU/vw3As5Xj7/Z9v+H7/oMA7gcfTxYjAt/3vwBgPnbYaDwwxi4AUPV9/ys+N297u3KOxRZHjzHSC3aMbDP4vn/M9/1vBj8vA7gbwG7YecQCfcdHL9jxsY3gc6wEvzrBfz7s/GERoM8Y6QU7RkYMNgk0OHYDeEj5/TD6L8AWow0fwCcYY7cxxm4Oju30ff8YwAM2ADuC43bsbE+Yjofdwc/x4xajjRcxxr7DuFxMUPXtGNnGYIztBfBoAF+DnUcsYoiND8DOIRbgigXG2O0ATgL4pO/7dv6wiKDHGAHsHLItYJNAg0Ond7St1rYvnuD7/mPA5YH/mzH25D5/144dCxW9xoMdJ9sP/wLgMnBq9jEAfxcct2Nkm4IxVgbwfgC/4/v+Ur+/qjlmx8iIQzM+7BxiAQDwfb/j+/51AC4CZ2w8os9ft+NjG6LHGLFzyDaBTQINjsMALlZ+vwjA0fN0LRbnGb7vHw3+fxLAB8HlXScCmiSC/58M/rodO9sTpuPhcPBz/LjFiML3/RNBUNYF8EaEMlE7RrYhGGMO+AZ/1vf9DwSH7TxiAUA/PuwcYhGH7/sLAD4H7tNi5w+LBNQxYueQ7QObBBoc3wBwBWNsH2PMBTfL+vB5viaL8wDGWIkxVhE/A/hRAHeAj4dfDf7arwL4UPDzhwH8AmPMY4ztAzdR+/rmXrXFeYDReAio2suMse8POi38inKOxQhCBOcBfhJ8HgHsGNl2CL7PfwNwt+/7r1b+yM4jFj3Hh51DLACAMTbNGBsPfi4A+GEA98DOHxYBeo0RO4dsH+TO9wVsVfi+32aMvQjAxwFkAbzZ9/07z/NlWZwf7ATwwaAjYg7Au3zf/xhj7BsA3ssY+w0AhwD8LAD4vn8nY+y9AO4C7/Dxv33f75yfS7fYCDDG/h3AUwHUGGOHAfw5gL+B+Xh4AXgXqQKA/w7+sxgB9BgjT2WMXQdOpT4A4HmAHSPbFE8A8MsAvht4NgDAn8DOIxYcvcbHc+wcYgHgAgBvC7o3ZQC81/f9jzDGvgI7f1hw9Boj77BzyPYA40beFhYWFhYWFhYWFhYWFhYWFhajDCsHs7CwsLCwsLCwsLCwsLCwsNgGsEkgCwsLCwsLCwsLCwsLCwsLi20AmwSysLCwsLCwsLCwsLCwsLCw2AawSSALCwsLCwsLCwsLCwsLCwuLbQCbBLKwsLCwsLCwsLCwsLCwsLDYBrBJIAsLCwsLCwsLCwsLCwsLC4ttAJsEsrCwsLCwsLCwsLCwsLCwsNgGsEkgCwsLCwsLCwsLCwsLCwsLi22A/x8gNcMalNTYKwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,5))\n",
    "plt.plot(ppg)\n",
    "heights_peaks = ppg[peaks]\n",
    "heights_onsets = ppg[onsets]\n",
    "plt.scatter(peaks, heights_peaks, marker='o', color='green')\n",
    "plt.scatter(onsets, heights_onsets, marker='o', color='red')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "GxPNFzVz8G8l"
   },
   "source": [
    "## Find Diastolic peak and Dicrotic notch"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "AJ6nWn0B9mmt"
   },
   "source": [
    "### Differentiation Demo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "rUVExyk08_7R",
    "outputId": "669c9b84-260f-4d16-9b35-82ccc5833f74"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fde7c7e0f40>]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ppg_seg = ppg[0:200]\n",
    "seg_peaks = get_peaks(ppg_seg, fs)\n",
    "seg_onset = get_onsets(ppg_seg, seg_peaks, fs)\n",
    "d1x = signal.savgol_filter(ppg_seg, 9, 5, deriv = 1) \n",
    "d2x = signal.savgol_filter(ppg_seg, 9, 5, deriv = 2) \n",
    "d3x = signal.savgol_filter(ppg_seg, 9, 5, deriv = 3) \n",
    "fig, axs = plt.subplots(4, figsize=(20,20))\n",
    "axs[0].plot(ppg_seg)\n",
    "axs[0].scatter(seg_onset, ppg_seg[seg_onset], color='red')\n",
    "axs[1].plot(d1x)\n",
    "axs[2].plot(d2x)\n",
    "axs[2].scatter(seg_onset, d2x[seg_onset], color='red')\n",
    "axs[3].plot(d3x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "d7GITTev9181"
   },
   "source": [
    "### Actual Function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "id": "MknYeqUO3yjo"
   },
   "outputs": [],
   "source": [
    "import scipy.signal as sp\n",
    "\n",
    "def get_dia_dic(ppg, pks, ons, fs):\n",
    "    d1x = sp.savgol_filter(ppg, 9, 5, deriv = 1) \n",
    "    d2x = sp.savgol_filter(ppg, 9, 5, deriv = 2) \n",
    "    d3x = sp.savgol_filter(ppg, 9, 5, deriv = 3) \n",
    "\n",
    "    dia = np.empty(0)\n",
    "    dic = np.empty(0)\n",
    "    for i in range(len(ons) - 1):\n",
    "        start = ons[i]\n",
    "        stop = ons[i + 1]\n",
    "        ind_pks = np.intersect1d(np.where(pks < stop), np.where(pks > start))\n",
    "        if len(ind_pks) != 0:\n",
    "            ind_pks = ind_pks[0]\n",
    "            ind_pks = pks[ind_pks]\n",
    "            ibi_portion = ppg[ind_pks:stop]\n",
    "            ibi_2d_portion = d2x[ind_pks:stop]\n",
    "           \n",
    "            aux_dic, _ = sp.find_peaks(ibi_2d_portion)\n",
    "            aux_dic = aux_dic.astype(int)\n",
    "            aux_dia, _ = sp.find_peaks(-ibi_2d_portion)\n",
    "            aux_dia = aux_dia.astype(int)   \n",
    "            if len(aux_dic) != 0:\n",
    "                ind_max, = np.where(ibi_2d_portion[aux_dic] == np.max(ibi_2d_portion[aux_dic]))\n",
    "                aux_dic_max = aux_dic[ind_max]\n",
    "                if len(aux_dia) != 0:\n",
    "                    nearest = aux_dia - aux_dic_max\n",
    "                    aux_dic = aux_dic_max\n",
    "                    dic = np.append(dic, (aux_dic + ind_pks).astype(int))\n",
    "                    \n",
    "                    ind_dia, = np.where(nearest > 0)\n",
    "                    aux_dia = aux_dia[ind_dia]\n",
    "                    nearest = nearest[ind_dia]\n",
    "                    if len(nearest) != 0:\n",
    "                        ind_nearest, = np.where(nearest == np.min(nearest))\n",
    "                        aux_dia = aux_dia[ind_nearest]\n",
    "                        dia = np.append(dia, (aux_dia + ind_pks).astype(int))\n",
    "                else:\n",
    "                    dic = np.append(dic, (aux_dic_max + ind_pks).astype(int))\n",
    "        \n",
    "        break\n",
    "    dia = dia.astype(int)\n",
    "    dic = dic.astype(int)\n",
    "\n",
    "    return dia, dic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ZQOIfXbYCEhC",
    "outputId": "c19fdf81-0f4e-4457-a2eb-ed4fe016cc18"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7fde79e24340>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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9QUl2GqlJJ5/daKwI7zI6JrkZdnVi2LvgIAtC54BDfWO4xqRArdXNt29htNWlocrvh/okyLISCbJsosvtoTL/1PlYEZFlJWmvsTgcGxqnPO/UJ+CctGSqCzIkN8PGFlLtPVrkHCB9DK1vvi11otUWZuJ0KJnJshgJsmyic3CcqoJT52NFrFmWTUaKU5aNFoke9zhlebG9/41S/dm2ApNB+ke98672Hm19ZR5JDiXJ7zYw35Y60VKSHNQWZnCwdyRewxJxIEGWDQxP+Bka989pJivJ6WBDVZ6cYBcBrTXHhiYoj7FuUmN5Dh0DUv3ZjvpHfWgNJXGYyUpPcdJYkSvJ7zbQO+wlI8VJZmpMpStnVVeSLS3VLEaCLBvodoV2FlbNIciC0DbuvceGGfMGjBiWSJDBMR/eQJCy3FhnskKFSPfKbJbt9IdnNIpi3OByKk3V+ezscuMLBOPyeMIYCylEGq2uNIuOAY/sMLQQCbJsoHMwVCMrlvIN0TbV5BPUsKPTbcCoRKIcC29eiCUnC95d/VnYy2A4Sb0wa+5NgmfSXJuPNxCUHD2L6xuZWFA+VsSqkiwmg5r2fk8cRiXiQYIsG+iKzGTFUIg02sbqfJSSoqR21xOu9l8eY05WcXYqRVkpssPQhiJBVn5GnIIsKUxsC70LqPYeLdJ2rU2WDC1Dgiwb6HR5yEhxkp+RPKf75aYns7okW06wNhcJsmJdLlRK0VCeKzNZNjQQmcnKjE+QVZKTRlVBujSLtrhQtfeF5+GtLM5CKST53UIkyLKBLtfcamRFa6rNZ/tRF8GgFCW1q2NDE6Q4HXP64m0sz+HgiRHJzbCZwTEvTociN31uF1Qns6Eqn7e7ZbnQqib8k4xMBOIyk5WW7KS6IEOS3y1Egiwb6Bz0zDnpPaKpOp+RiYB86GysZ2iCZblpOByxB9mN5VL92Y4Gx3zkZyTP6b0+lcbyHLrd41KU1KIihUjjEWQB1JVk0Safe8uQIMvitNZ0h2ey5qO5NtIoVoqS2tUxd2yFSKNFdhhKwrO9DI75KIjTUmFEZCOE5OhZU6RGVqwt005lVUk2h/tHCUzKjlIrkCDL4obHA4x4A3NOeo+oLsigKCtF8rJsLFQja25Bdk1BBpkpTsnLshljgiwJuK3M7QnNMObNMed2NnUlWfgnNR2DssPQCiTIsrhO1/zKN0QoFWoUK0GWPU0GNceHJyib40yWw6FoKM9hZ5d8sdrJgAFBVkFmCmW5aRJwW5TbEyoaHK8dpXWloR2GkipgDRJkWVzXVJA1v5ksCPUw6xjwTK39C/voHZlgMqhj3lkY7bxVRbzV5Z4qCyCsz4iZLJBWS1bmCgdZ8ZrJWlkcKeMgOwytQIIsi4vUyJrvTBZAU00BILVy7KjHHSpEWhFjjaxoW+pL0BpePNAX72EJAwQmg7g9fgoy45ObE62hPJfDfaOM+2S3qdUMeXwoBdlp8QmyMlOTqMhLl81OFiFBlsX1jnhJTXIsaEv3aRU5pDgdbD8qQZbdHBsK18ia43IhwLqKXAozU3hhf2+8hyUM4B4PzWjEq0ZWtMbyHIIa9h2X2SyrcY/7yU1PxhnHHaV1pVmyXGgREmRZXP+Il6Ks1HnVyIpITXKyrjKXN9plh6HdHAvPZM1nudDhUFxUX8y2A31MSp00y4ss6xqxXNhQJq2WrMrl8ZMXx7poEEp+P9Q3Kp97C5Agy+L6Rr0UxaGP2YV1xezodE/leAl76BkaJzPFSU5a0rzuv6W+BLfHL/0rbWBgNL7V3qNV5qeTm54sQZYFuT0+cuOU9B5RV5KNNxCU870FSJBlcQOjPoriUD/l2qYKAB5o7V7wY4nE6XGPU5Y3v2r/EAqunQ7FVlkytLypmaw4NYeOppSioSyHPVLGwXKGxv1zbpl2Kqtkh6FlSJBlcf2jXgrjcNKtzM/gvJVF3NfaKS12bOTY0ETMjaFnkpuRTFN1Ps/vkyDL6gbHQrt/C+I8qxHRWJ7DvuMjUqTSYlweX9yXCyONoiX53XwSZFlYMKgZHIvPTBbA9c2VdLnGee3IQFweTxivxz1Bee7CGsduXlPM7p5hTgxPxGlUwgiR5tD5BiwXAjRW5OANBDnUN2bI44v5cXv85MU5sM5JS2ZZThptEmSZToIsCxsa9xMI6rgFWZc0LiM7LYn7Wrri8njCWL5AkP5RL8sWGGRdtLoYgFcPSXBtZa4xHzlpSSQ7jTktN5SFKr/vlfY6lhGYDDIyEYhbjaxodaVZUivLAmL6NCulLlVK7VdKtSmlvjjLMZuVUjuUUruVUtvmcl8xs4Hw8kE8lgsh1KH96g3lPPH2MYYn/HF5TGGcyPtfkr2wIGt1aTYpSQ5pq2JxA2M+CuN0QTWT5UWZOB1KZjcsZChctiPey4UQWjI82DuK1pIeYqZTBllKKSfwA+AyoAG4SSnVMO2YPOCHwIe01o3A9bHeV8yubyS0fBCvxqEA1zdV4Q0EeXRnT9weUxhjarfZAoPsZKeDNcuyZWeZxRlV7T0iJclBTWEGB2V2wzIitdHivVwIoSDL45ukZ0jSBMwUy0zWmUCb1vqw1toH3ANcPe2YPwAe1FofBdBa987hvmIW/aORmaz4BVmnV+ZSX5otS4Y20Bd+/+OxXNxYnsOeY8NyVWthRgdZEKqfJDNZ1uGOc0udaHUl2QAcPCFBtZliCbIqgM6on7vCt0VbDeQrpbYqpVqVUn88h/uKWQxMfcnG78SrlOL65kp2dLo5IB8+S4vMZMXj/W8oz8Xt8ctVrYUNjPkM21kYsaoki44BD76A7DC0Arcn9Bk3YiarriTSw1CCajPFEmTNVKBn+uVwEtAEXAFcAnxZKbU6xvuGnkSpW5RSLUqplr4+6bUG0D/qw6Hi/wG8ZmMFSQ7FfS2dpz5YmKY/zjNZALu7JS/LirTWuMZ8htTIiraqJItAUNMxIDsMrWBqJsuAnKz8zBSKslKkVpbJYgmyuoCqqJ8rgekJPV3AU1rrMa11P/AisD7G+wKgtb5La92stW4uLi6OdfyL2sCYl4LM1Lj2tILQ8uPFa0t46M1u/FIzx7IGRr2kJzvJTJ1ftfdoa5Zlo5S0VbGq4YkAgaA2pNp7tMgSksxuWEMkJyvfoBnMUPK7rFiYKZYg6w2gTim1XCmVAtwIPDLtmN8AFyilkpRSGcBZwN4Y7ytm0Tfii+tSYbQbmqvoH/XxghSptKz+UV/cdpZmpCSxoihTgiyLMrJvYbQVxZmAFKm0iiGPD6Uge55ts06lriRbdhia7JRBltY6AHwWeJpQ4HSv1nq3UupWpdSt4WP2Ak8BbwGvA/+jtd41232NeSmLz8CYN241sqa7aHUxxdmp3CsJ8JbVPxrf97+xPFfaqljUVLV3g4OsjJQkKvPTZSbLIlweP7npyTjivFoRUVeaxchEgN4RryGPL04tpvBZa/0E8MS02+6c9vO3gW/Hcl8Rm/5RLzXVGYY8dpLTwdXry/nZK+1M+CdJS3Ya8jxi/vpHfVTkLaxGVrTG8hwe2dmDa8xnWFVxMT/vNIc2rk5WRKR+kjCfe9xv2FIhRLXXOTFKaU78ziUidlLx3cIGRo0tTrixOp9AUEtipEUZMZMFsEcqfluOkc2hp6sryeJw3yiT0sPUdG6Pj1wDkt4jpso4SF6WaSTIsiiPL4DHN2nYciFE7TiTJSTLiXffSoAGeb8tK9K30OgSDhCa3fAGgnS5PIY/lzi5UN9C44KsoqwU8jKSZebSRBJkWVT/SHyqfZ9MdUEGWalJMrNhQe5xP5NBHdf3vyAzhbLcNEl+tyDXmI/0ZCfpKcYv26+SHYaW4R73GbpcqJQKFaCV1QrTSJBlUf3hRNh4ttSZzuFQrC2TditWNBDHGlnRGstz2CW1siwnEdXeI1ZJkUrLcIcT3420qiRblgtNJEGWRfWPxLc59Gway3PZe2xY8jMspm/UmPd/Q1Ueh/rGGPJIg3ArcXl85Gca+2UbkZueTEl2qiwhmSwwGWRkImDociGEcvBcHv/UhZtILAmyLCqSo2FkThaE8nQ8vknapQK0pfSPxr85OMCmmnwAth91xfVxxcIMjRs/oxFNdhiab8jgQqQRdaXhHYbyfptCgiyLStxMViQZWpYMrcSo5cINVXk4HYrWDgmyrGRo3E9eeuLKaqwuzebgiRGZwTZRpNq78TNZkR2GEmSZQYIsixoY85GdlkRqkrGJsHUl2SQ7lew4s5j+US9Oh4r77EZGShINZTm0dAzG9XHFwgyN+8lJ4EyWzGCbL9Ic2ugZzNKcVLJTk2g7IXlZZpAgy6L6Rr2GJr1HpCQ5WF2azR6ZybKUgVEfhZkphlSCbqrJZ2fnkPSttAitdcKXCyMz2PK5N0+kObTRy4VKKVaVyvKwWSTIsqj+Ea/hS4URjeU57OkZlv5WFtI/6jWsEG1TTT7j/kn2SukOSxj3T+Kf1IYvG0V7ZwZb/g2YJRJkJeJ9X1UsQZZZJMiyqIE4F6I8mYayHAbGfJwYlt0nVtE/alxz8ObaUPK75GVZQ+TLNpEzWZEZbEkTMI8rvFyYiFy8utIs+ka8U0uUInEkyLKo0ExGgmayKkLtVuSEax39Bi4Xl+WmU56bRosEWZYQ2WWWyCALZAbbbEPjfhwKstNiaiG8IHVSgNY0EmRZkH8yiNvjT0izWIC1ZTkoJTsMrUJrbXiQ3VRbwHYJsiwhEmTlJTzIypUZbBNFCpEakXc53VSjaAmyEk6CLAuKLB8kaiYrKzWJyvx0+QBahMc3yYQ/aOhycVN1HseGJuh2jxv2HCI2kc97IncXgvSyNJvL4yMvAb0qASry0klPdnJQ2usknARZFhRZqzd610m0upJQ3Rxhvv6pau/GBVnNtQUAtLRLKQezDZu0XCgz2OYaGje2OXQ0h0OFC9DKOT7RJMiyINeYGUFWFof7xwjItn7TRaq9G5X4DrBmWTYpSQ7pY2gBQwkqSjldVmoStYWZUsbBJG6PP6FLxCuKMznSL3XREk2CLAuamslKUC8zgJUlWfgCQTpdsnxktn6Dqr1HS3I6WLNMmoNbgXvch9OhyEo1PgF6uobyHHYfk0DbDIlcLgSoLcykxz2ONzCZsOcUEmRZkitBReqi1YUTI2X3ifkGRhPTt7KxPIc9x2R3mdmGxv3kpCWhlPEJ0NM1lufQOTg+NZsmEmfIk7jlQoDaogyCGjoH5UI6kSTIsqBBE5YL39l9Imv2ZovMZBVkGvv+N5Tl4Pb46RmaMPR5xMkNjQcSOqMRrbE8VL5FlgwTyz8ZZMQbSGi/yprCTAA6pJVSQkmQZUFuj4+0ZAfpKcb2LYyWnZZMWW4abbL7xHQDo15y0pJISTL249kQ/oLdLXlZpnJ7fAnfWRjRUBbaYSjV/xPLjDy85eEgS/KyEkuCLAtyefwUmHBlG9p9IkGW2Vwev+GzWABry7Jld5kFDCe4b2G0oqwUstOSpFF0giWypU5EXkYyOWlJdAx4EvacQoIsS3KNJTYhMqKuJJu23lGCQcnRMZN73E9uAt7/jJQkVhRlSpBlskQ3h46mlGJ5kew6S7Sh8XBLnQSe5yPvtQTUiSVBlgW5PL6EzGRMV1eaxbh/UgpUmmzI4yM/QVe4jeW5slRksqHxxG7ln66mMFNmNxLMNWZOlf+aQgmyEk2CLAtyJXjXSYTsMLQGVwLr5zSU59DtHp+qzSYSKxjUps5kASwvzKDL5cEXkBp5ieIeT/wOcoDawgy6XePyXieQBFkWZNZMluwwtAZ3AuvnNIZbq+yR2SxTjPoCBHXiq71Hqy3KDG3td8lsVqK4w7UQcxN8MS3vdeLFFGQppS5VSu1XSrUppb44w+83K6WGlFI7wn++EvW7dqXU2+HbW+I5+MVoMnxla0ZOVl5GCsXZqdLfykSTQc3wRCBhM5mRLfzSv84cQ+EE6ER/2UarLQrtOmuXvKyEcXv8OBRkJ7gArZRxSLxTvsNKKSfwA+ADQBfwhlLqEa31nmmHvqS1vnKWh9mite5f2FCXhqFxP1pDgUkn3VXFWbT1SZBllqmt3Qma2SjITKEsN03qJJlkyKS+hdFqw1+87ZKXlTDu8dBstcOR2AK0tYUZABzpl/c6UWKZyToTaNNaH9Za+4B7gKuNHdbSNVWI1ITlQgglv7edGJUq4CaJLCMkciazsTxHdhiaxApBVn54a7/MZCVOovsWRhRkhkp2yExW4sQSZFUAnVE/d4Vvm+4cpdROpdSTSqnGqNs18IxSqlUpdcsCxrokRL5kE50QGVFXms2INyBVwE3iMqF+ztqyHA71jTLhl55miWZWc+hosrU/8dwevylLxEopagulZEcixRJkzTSfOX2aYztQo7VeD3wPeDjqd+dprTcBlwGfUUpdOOOTKHWLUqpFKdXS19cXw7AWJzNa6kSLVICWKuDmMKN+Tl1pNkGNfMmaIFKU0syZLAjl6sgXb+K4x32mneNri6RkRyLFEmR1AVVRP1cCPdEHaK2Htdaj4b8/ASQrpYrCP/eE/9sLPERo+fE9tNZ3aa2btdbNxcXFc34hi0XkpJufac5JV6qAm2uqEnQCv3QjpTtkw0PiWWG5EEJfvD3ucbwBmc1MBLOWCyGUlyUlOxInliDrDaBOKbVcKZUC3Ag8En2AUmqZCreQV0qdGX7cAaVUplIqO3x7JvBBYFc8X8BiM2jycqFUATdXZLkwke//8qJMHAppqWSCoXE/KU4H6cmJ61M6k+VFGaGt/YNSiDgRzFouhNCsZVBDl5RxSIhT7i7UWgeUUp8FngacwE+01ruVUreGf38ncB3wKaVUABgHbtRaa6VUKfBQOP5KAn6ptX7KoNeyKLg8PlKSHGQksDn0dI3lubS0D5r2/EvZkMcX2tqdlrit3WnJTmoKM2mT+mgJNzQeag4dPkeaJrK1v71/bKpenjCGfzLIqDdg2oX08qLQDsP2gTFWFMt7bbSYzuThJcAnpt12Z9Tfvw98f4b7HQbWL3CMS4prLNRSxcyTbmN5Do/s7AmNxaRdjkuVO1z9O9Fbu1eVZMlyoQlC1d4TWytpJsunyjhIXpbRzN7ssKIoFFgd6h3jfWtMGcKSIhXfLcbl8Zt2hRPRIFXATRNqqWRGc/AsjvSP4Z+UPI1EMqvw8HT5mSnkpidL8nsCTFV7NyknKz8zhaKsFOnskSASZFlMaCbL3JOuVAE3j9vjM+XkW1eaRSCopX5Ogrk95vYtjCa7zhLDbULe5XSrSrIkBzNBJMiyGLP6FkaLVAGX5PfEc3v85JvSHDwbkB2GiWZ2c+hotYUZMpOVAG4TauFNV1eSLUWnE0SCLItxe/ymfvgipAq4OSLtNhJtZXEWSnYYJpyVgqyVxVn0DI0z5g2YPZRFzRXp6pBu3sV0XWkWI94AJ4a9po1hqZAgy0KCQW2JmSyAhvJcDveNMu6TujmJZNbyUXqKk8r8dAmyEmgyqBmZCFgmyGooy0Fr2Cu5mIaaqo1m4sV0ZAep5GUZT4IsCxmZCBDUia32PZvG8hyCGvYdlxNuogQmg4xMmLe1u64km4Mn5KSbKMMWKUQa0VgR7vYgM9iGcnv8OB2KnASWaZkukh7QJhdVhpMgy0IihUgLTKr2Hm2qvY6ccBPG7K3ddSVZHO4fIyA7DBPCKtXeI5blpFGQmSIbXgzmCm9uMbNMT1FWCnkZyTJznQASZFnI1Fq9BWayKvPTyU1PljIOCWRGc+hoq0qy8AWCdLqk6nciWC3IUkpJLmYCuMfNa6kToZSiriSLNtnoYjgJsizEFW4OXWCBIEspxYriTI70yW6jRDGjOXS0utLIDkNZMkyESDP4gizzP+8RDeU5HDgxIn3tDDRkkc1Nq0qyONA7IjsMDSZBloWY0bfuZJYXZkoF6AQyozl0tJXFoarfbX1ydZsIA+Egq9ACG10iTivPxT+pOSCBtmFcHnN2EE+3qiQbt8c/9e9QGEOCLAuJzGTlWyAnC0LFCY8NTcgOwwQxO8jOTkumLDdNlhASZHAstH3eCruJIxoj3R5kydAwbo/5y4UQysEEqY1nNAmyLMTl8ZHkUGSlmt/LDEJBFsDRQakCnQhT7TZM3totybCJMTDmI8XpsMznHaC2MJPMFKckvxvIKq2U6kpDQZY0hjeWBFkWMhhuyGzmrpNotYWhbu1SBToxhsb9OBRkm/ilW1eSTVvvKMGg5GkYbXA0VBPPKp93AIdDsbZMkt+N4p8MMuoNWCIna1lOGlmpSXJRZTAJsiykf9RHUVaq2cOYEpnJkrysxIjkajgc5n3p1pVmMe6fpNstOwyNNjhmjcLD0zWW57D32LAE2gawQkudCKVUaOZalgsNJUGWhfSPeimy0E6jnLRkCjNTaJeZrISwQq5GJE9DihQab2DMR6GFPu8RjeW5jPkm5eLKAGbvIJ5udWkW+0/IDkMjSZBlIaEgyzozWQA10jQ2Ydwev6n5WCDtNhLJsjNZUvndMGbvIJ6uoSyHwTGf9DA0kARZFjIw6rPUTBaElgw7BiTxPRHc4z7Ty3fkZaRQnJ0qSwgJYNUgq64km2SnYpckv8ed2QWHp2usyAVgV7e810aRIMsixrwBxv2TFFpsJmt5YSbHh6WMQyJYYbkQQkuGkgxrLG9gklFvwFI1siJSkhysLs2WMg4GiHT1MPtiKmJtWQ5KyaylkSTIsoiB0dCHz2rLhZL8njhWWC6EUJDV1jsqeRoGmqr2nmmtz3tEpL2O/BuIr3dqIVojyMpKTWJ5YabMWhpIgiyL6BsNrYlbLRG2tjAcZElelqEiW7utcIW7qjSbUW+A48MTZg9l0YpcVFlxuRBCye+DYz75NxBng55QbbTMFKfZQ5nSWJErs5YGkiDLIgbCQVax5WaywrWyZCbLUFba2i2VoI0Xmcmy2kVVRKTy++5u+fKNJ/eYn/zMZEvVRmssz6HbPT41yybiS4Isi+gfteZJNzstmaKsFDr6JfndSFPV3i2SkwVIXpaB3lkutNbnPUJydYwx6DF/c8t0p5WHkt/lvTaGBFkW0R9ZLrRgjkZtYabMZBkssuvICl+6hVmpFGSmSLsNA1mxOXS0zNQklhdlSnudOHNbMMiamrWU99oQEmRZxMCol5y0JFKSrPeW1BRmSk6WwSIzG1Y5AUslaGMNjnlxOhQ5aebPXM6msTxXZjfiLNQ6zVrveX5mChV56eyS99oQ1vtGX6L6R30UZVtvFgtgeVEGvSNexrwBs4eyaEWWC60wkwXvlHGQ3WXGGBwLzWiY2ULpVCRXJ/7cHr9lLqSiNZTnyEyWQSTIsggrVnuPiFQB33dcrnSMMmix+jlrlmUzNO6nyyU9DI0wOOaz7FJhRGQZac8x+dzHQzCocXmsWYD2tPJcjvSPyYW0AWIKspRSlyql9iul2pRSX5zh95uVUkNKqR3hP1+J9b4ixGp9C6OdvaIQh4Jt+/vMHsqi5fb4SUt2kG6Rrd3nrioCYOsBec+NYNVq79EapxKiZYYjHoYn/AS1dfoWRmssz0Fr2CsBddydMshSSjmBHwCXAQ3ATUqphhkOfUlrvSH852tzvO+S1z/qs+xMVl5GChuq8tgmX7iGiSwfWcWKokyqCtLZtr/X7KEsSgNjPgoselEVUZCZQlluWsLysu5++25qv1uL46sOar9by91v352Q502Udza3WCsnC+C0cHudt7okoI63WGayzgTatNaHtdY+4B7g6hgffyH3XTJ8gSBD435L7iyM2FxfwlvdQ1P1vER8WW3XkVKKzatLeOXQAN6AtFSKt8ExHwUWer9nk6jk97vfvptbHr2FjqEONJqOoQ5uefSWRRVoRTa3WHEma1luGmW5aWw/6jJ7KItOUgzHVACdUT93AWfNcNw5SqmdQA/wd1rr3XO475IW+fAVZVvvwxexub6Y7zx7gBcP9nHNxkqzh7PoWHHX0eb6Yn7xWgct7S7OCy8fioULTAZxe/yWXy6E0DLSc/tOMDzhj/tOyIfe7OIrv9lNYFIzHkinUP8vhUBQDdGX/G087OO2527j5nU3x/V5zTK1ucWCQRZAU00+2zuMDbL+56XD/MezBwjOsp9GKbihuYovX9mA08KbQuYiliBrplc6/X/RdqBGaz2qlLoceBioi/G+oSdR6hbgFoDq6uoYhrV4WLlGVsRp5bkUZqawdb8EWUZwe/yU56WbPYx3OWdlISlOB1v390qQFUeRZSOrFR6eyeb6Yv7fcwd54q1j3Hhm/M7LQx4/X310D1X5GZxfV8S/vfJv6PBXQ8bkOZT6vk5fytc5OrQzbs9pNqsXoG2qyeext47R4x435FzUOejhW0/tZ31VLhur82c85tjQBD97pZ3+US/fuWGDJUsazVUsQVYXUBX1cyWh2aopWuvhqL8/oZT6oVKqKJb7Rt3vLuAugObm5iW1bzwSZBVbeCbL4VBcuLqYrft7mQzqRXOVYRVWrASdkZLEmcsL2Lq/j9uuMHs0i4fVv2yjbajKY2VxJve3dsU1yPre8wcZGvfzyz89m4byHO468BwdQx0ADCc9RKn3a5T4bkfl/Thuz2k2K7XOmklzTQEArR0uQ4Ksbzy5D6dD8b2bNrEsN23W49ZV5PAvT+xjZCLA/3ysmWSnvQOtWEb/BlCnlFqulEoBbgQeiT5AKbVMhZsxKaXODD/uQCz3Fe80i7XyTBaErmpdHj9vd0tyZDxNBjVD437yLfilu7m+mIO9o3S7pZRDvAyMhS6q7BBkKaW4vrmKlg4Xh/viU5y2Y2CMn7/azvVNlTSEy0TccfEdZCSH+qQGlZvjqf9AwHGUzPFbGJ7wx+V5zTbo8ZHsVGSlxjK3kXhryrJJT3bSasCS4etHBnn87WPcetHKkwZYALdcuJKvf/g0th3o4/9e64j7WBLtlEGW1joAfBZ4GtgL3Ku13q2UulUpdWv4sOuAXeGcrP8EbtQhM97XiBdiZ5GZLKsWI424oK4YpWCr7DiLq6FxP1pDvgWvcDfXFwPynsfTVHNoi19URVyzsQKHgge2dy34sbTWfOPJfSQ5HPztB+unbr953c3cddVd1OTWoFBU5xXxV5eU4PE5+P7zbQt+XitwjfnIy0ixVHPoaMlOB+urcuMeZAWDmn9+bA9luWnccuGKmO5z81nVXFBXxH88e2Dq82JXMc3Daa2f0Fqv1lqv1FrfEb7tTq31neG/f19r3ai1Xq+1Pltr/crJ7iverX/US2qSg0yL1EiaTUFmCusqcnnl0IDZQ1lUrLx8tLI4i4q8dF5u6zd7KIuGld/vmZTmpHHR6mIeaO1mcraM5RgEJoN8+Te7eHLXcT61eSWlOe+e0bh53c20f66d4O1B2j/Xzt9fdCPXN1Xy05ePcGQRtPVyeay/o7S5poA9x4bx+OJXlPTBN7t5u3uIL1y6JuY6gEopvnJlA2O+Sf79mf1xG4sZ7L3YuUgMhGtkWfUKJ9q6ilz29gwTXMDJVrxbZNeRFbd2K6Vors2ntcMlLXbiJJIeYMWZy9lc11TF8eGJeQfbo94Af/a/Lfzfa0f584tW8Nktq2K6399dUk9qkpM7Ht87r+e1EteY33I7iKdrqslnMqjZ0emOy+ONeQN8++l9bKjK40Pry+d037rSbP7o7Bp+9fpR9ti4r6IEWRbQN+q1/FJhRGN5LiPegLRbiaOpmQ0LBlkQOvGeGPbKex4ng2M+8jKSSbJRQu/7G0rITU/m4Te753X/LzzwFi8e7OdfrlnHP1y2NuaejSXZaXxmyyp+u/cEvzto79lUlwU3t0y3KbzrL16lHH607RAnhr18+cqGefXp/Ov3ryY3PZmvPbbbthd59vmUL2IDoz6KbLJ0EOlnJq024sfqu46aasInXilUGBd2aKkzXWqSky31xWw70DfnWezXjwzy+FvH+Iv3reIPzpr7DsVPnl9LdUEGX3tsN4HJ4JzvbxUuj8+Sm1ui5WYkU1eSRUscgqxu9zg/evEwH1pfPnUOmc94/vaD9bx2eJAndx1f8JjMIEGWBVi5OfR09cuycTpUwlptLAWR5tBW/eKtL80mM8VJS7sEWfEwMOa1fHPomVxUX8zAmI9dc7jAik56/vMLV87reVOTnHzp8rUcODHKr14/Oq/HMJvWGpfHb4sl4ubaUFHShaaEfPfZAwB84bI1C3qcm86sZs2ybO54fC8Tfvt1n5Agy2TBoGZgzGeLwoQAaclOVhVnyUxWHLk8PlKSHGRYdONDktPBxup8Q7Z2L0UDFu5TejIXTu0ujr2H6XySnmdySWMp56wo5DvPHmDIY7+SDsMTASaD2vLLhRBaMhyeCNC2gJIdwxN+Hn2rh2ubKqlYYM0tp0Nx+1WNdLvHuevFwwt6LDNIkGWyoXE/k0Ftq5NuY3mOzGTFkWvMR35GsqU3PmyqyWff8WFGvfHbdbRU2emiKlphViqnV+TGXM5jzBvgW0/NL+l5OqUUX7mqgaFxP9997sCCHssMLhvtKG2ufaco6Xw9tvMYE/4gNzRXnfrgGJyzspDL1y3jh1vb6B2eiMtjJooEWSaLFCa000m3oTyH3hEvfSPSLDoeQssI1n7/m2vyCWrYcdRt9lBsLTAZxOXx2aZG1nQX1Zewo9M9tSP2ZH607RC9I/NPep5ubVkON55ZzS9e7aCtNz6FURPF5YnsKLX25xygtjCDwsyUBQVZ97Z0sro0i/WVuXEb1+cvWcOEP8h9rQuv15ZIEmSZzC7V3qNFqjTvOSazWfEQmsmy9sl3Q3UeSkFLx6DZQ7G1QY8Pra1feHg2F60uJqjhxVPs9ItH0vNM/vYDq0lPcfL1x/fE7TETYSrIssFMllKKTTXzTw84eGKEHZ1ubmiuiuvs/PKiTM6sLeCB1i5b7TSUIMtkditMCNBYFro6kbys+HB5rL/bLCctmfrSbMnLWqD+kdDn3S67iadzvtRJmneS//zSs3y39ru8fffbMx73zSf3AQtPep6uMCuVv7q4jq37+3jBRl0IXGOhPDI7JL5DaEfxkf4xBkbnvlpxX2sXSQ7FhzdWxH1c1zVXcrh/zFY7nSXIMtmADYOs3IxkKvPTJS8rTlwev2XLN0RrqsnnzaPuBVX9Xuoi6QF2nMl6++63eeLPH6PskJvu5bm4O4Z49JZH3xNotXa4eGRnD7dcuGLBSc8z+eNzallRlMnXH9uD3yYlHew0kwWh9ACYe16WfzLIg9u7eN+aEkPyjC9fV0Z6spP7bbRkKEGWySIzWVavBDxdY3mOravwWkUwqHHbYCYL4IzaAka9AfbKMvG8RfqU2rGEw3O3PYff46fq0BDjWcm0bK7E5/Hz3G3PTR2z7/gwf/HL7ZRkp3LrRfMr2XAqKUkObrtiLYf6xvjFq/ZoIOzy+EhyKLIt2hx6utMqcklxOmid44zR1v199I/64pbwPl1WahKXryvjsZ3HGPfZo5yDBFkmGxzzkZ2aRGqSNbfvz6axPJcj/WOy22yBhif8BLU1W+pMd96qIsC4ZtH+ySDewOSsf+yUhzGbSA6mHWeyho6G0gOW7x1kzfZedp21jBc+vIK+4yN4A5NsO9DHdf/1KpNa89NPnEGmgQHF+9aUcEFdEd/9rT0aCA+O+S3dHHq6tGQnp1Xk0DrH2nj3tnRSlJU61VjeCNc1VTLiDfD0bnsUJ7VHWL2IDYz5KLDRzsKISOX3PT3DnLm8wOTR2Nc7OXnWn8kszk7l9MpcXtjfx2ffVxfXx350Zw9/c+8O/JOzB1KXNi7jv/5wk22+qGbSP+ojxemwzYxGtNzqXIY6hnBoOPvZo2S7vbyxpZKO+gLu/sengNAOwJ98vJmy3PgvE0aLNBC+9P+9xH88e4B//vBphj7fQrnGfLb4jEdrqsnn56924A1MxjQJ0Dfi5fl9vfzp+csNbRl11vICqgrS+f4LbZy9opBluWmnvpOJ7PdJX2QGx7y2WCqabn1VHgBvHnVJkLUArqmWOvb4N7C5voTvP38wtCMyTv9uRyb8fPXR3dSVZHPF6WUzHtPeP8Z9rV08tes4l62b+Rg7CHV3sM+MRrSL77iYR295FL/HjwJOe+MEpW4vBZ9qonTDMjJSnFzfXEVWggLIutJs/vCsan7xWgd/eHYN9cuyE/K88+Hy+GzzGY9oqingv186wq7u4Zh2iD78ZjeTQc31zZWGjsvhUPzrNafz579o4ZofvsyPP3bG1I53K5Igy2QDoz4q84296jNCUVYqtYUZtHS4+HOzB2NjLos3h55uS30x//ncQV482MfVG+Kze+h7z7cxMObjpx8/k3Wz1NUJTAZ5u3uIO57Yy5Y1JaQl22t5PWJg1EuhjQoPR1t38zoglJs1dHSI3OpcPnL7+6ZuN8Pn3r+ah3f08K9P7uVnnzjTtHGcisvjY3lRptnDmJNNNXkAvNE+eMogS2vNvS2dbKzOY1WJ8cHu+XVF3HfruXzyZ29w/Z2vcO+t59BYHr+aXPEkOVkms2Oz2IimmgK2d7gWRa6MWexUpBBgfWUehZkpvLAvPnlZh/tG+enLR7i+qXLWAAtCrX2+clUDXa5x/tuGrTUi+kftWe09Yt3N6/hc++e4PXg7n2v/nKkBFoR26/3R2TW8eKDP0u12Bsf8FNioFiJASXYajeU5PLqz55TH7uwa4mDvqGEJ7zNpKM/h4c+cR3qKk9t/s9uy30MSZJko1DTUZ7sPX0RTTT4DYz46BjxmD8W23tnabY98DYdDcdHqYrYd6FtwKQf/ZJDbH9lNapKTz19y6npK564s4rLTlvHDrYc4vIC+amYasFEzeLvYsqaEoIaX2mLvqZhIwaAOV/m3X3D90TOq2N0zzK7uk9dE/PUbnaQlO7hyluV+oyzLTePzl9TTEi4bYkUSZJloeCKAf1Lb8sMHoW7tAC1SoHLeXB4/SQ6VsDyWeNi8pgSXx8/OLve8H2N4ws8nfvoGLx3s5wuXraE4xt12X7p8LWnJDq6781VabVZ9Xmtt+5ksK9pQlUduevKcGlcn0vBEqD+tHVcsrl5fQUqSg3tbOmc95kj/GPe3dnLNxgqy0xJ/sXhdUxWnVeTwjSf34fFZb7e7BFkmsmO192irirPITkuSKuALEEkgt1Mi9IV1RTgU814yPDY0znX/9QqvHR7gW9edzh+dXRPzfasKMnjw0+eRm57MTf/9e554+9i8xmCGEW8A32SQYpnJiiunQ3FBXRHbDvRZcsnIjgWnI3IzkrnstGU8/GY3E/6Z61Ld8fgeUpwO/vr9qxM8uhCnQ3H7VY0cG5rgzq2HTBnDyUiQZaLBcPVnO5ZwgNDS0abqfNvNKFiJy+OzTdJ7RF5GCs01BTzx9rF5fand9tAuul3j/PyTZ84rh2N5USYPfupc1lXk8jf37uD40MScH8MM/SP2awZvF5vrS+gb8Vqyn6rdL6Y/2lzF8MTMdam2Hejjt3t7+YuL6yjJMa+Uwhm1BVx22jJ+9ko7AYt1AZAgy0TvNIe254cPQu0XDpwYZWh84UmnvSMTvNE++J4/bo/1iw3Ol2vMHi11prtmUwWH+sbY0eme0/1ebuvn+X29/OXFdVPFTecjPzOF7350A8Eg/Nsz++f9OIkUmdGQnKz4u2h1qPilFZcMI+d5uwZZZ68opKognV+82vGu8/LrRwb52qO7qS3M4BPn1Zo9TK48vZzhicCcz0lGs08iyCJk9yscYGpr75tHXWyuL5n347x2eIA//XnLjBXki7JS+d9PnmnpWijz5fL4WFWSZfYw5uyK08v46qO7ub+1i43Vp66hAzAZ1Hz98b1U5KXzsXNrFzyGqoLQyf2ulw7zifNqLbuFO2JqJsumG12srDg7ldMqcti2v4/PbFll9nDeJXKet+sMpsOhuPGMar799H6uv/PV9/z+f/642RIdS85fFUpj2Lq/j+Za69RulCDLRJErWzufdNdX5eF0KFo75h9kPbf3BJ++ezuV+el878qNJDvemWD1+ALc/shuPnrXq/z042dY6sMTD3YsUgiQk5bMpY3LeGRnD1++siGmulUPbO9i77Fh/vOmjXGrc/XpLau4t6WTOx7fy91/epalc9v6p2ay7Pd+28FFq4u5c9thhsb95KZbZ3Z4Ki3ExhfTf3bBCjZV579nR3FRdgprllnj4jc3I5lN1flsO9DH311Sb/ZwpshyoYkGx3ykJztJTzH/KmC+MlOTWFuWzetH5peXdV9LJ7f8opX6Zdncd+u5bKkv4fy6oqk/H2xcxv2fOpfirFT+8Me/57d7TsT5FZgnVMLDb7t2GxHXN1cxMhHgmRjekyP9Y3z76f1sqMrjqjhu885NT+Zz71/NK4cGuPv3R+P2uEYYGLX/l62Vba4vYTKoDeutOV+DY36ybNifNlpKkoNzVha+69x8fl2RZQKsiM31xbzdPURfeNbYCiTIMpHLxoVIo72vvoTX2wfpcY/HfB+tNf/+zH4+f/9bnLOikF/+2dmz/r+oyEvn3lvPYXVpNrf8ooWfvXwkXkM31fBEgMmgtk0h0unOWVFIRV4697d2nfS4N9oHueaHLzMZ1PzLNeviPtv0B2dVc+HqYv7x4V3829P7LbnDDEItdfIzkg3t67aUbarOZ1VJFv/x7AG8gZl3wpnBrq3T7CiymvLiAevk5smn3UQDY4ujZs51TVVozSm/bCO01nz+/rf43vNtfLS5ip9+4oxT1okqykrlnlvO5uK1pfzTo3v45lP74jF0U0Va6tg1yHI4FNduquClg310zxJgP7f3BDf/9+8pyEjhoU+fa0heXbLTwY8/1syNZ1Tx/Rfa+Lv73rJkoDUw6pOkdwM5HYovX9lA+4CHn77cbvZwpgwskotpO2goy6EoK5WtdguylFKXKqX2K6XalFJfPMlxZyilJpVS10Xd1q6UelsptUMp1RKPQS8Wdm6pE626MINzVxZyb0snwRiqgP92by/3t3bx6c0r+ca160iO8co+IyWJO/+wiY82V/FfWw/x5lF71+eyW7X3mdxwRhXJDgffeebAe3436g3wxQffZlVJFg9++lxqCo3r3ZbsdPCvH1nHZ7es4oHtXTxrwWXl/lHvoriosrKLVhfz/rUlfO+5g/SOWKO0x2I5z9uBw6G4cHURLx1ceEeKeDnlt5tSygn8ALgMaABuUko1zHLcN4GnZ3iYLVrrDVrr5gWOd1FZTB++j55RRZdrnFcPD5z0OG9gkq8/voe6kiz++gOr57x05HQovnxVAyXZqfzTo3tiCuqsym59C2dSmZ/BJ89fzgPbu96zdfr7z7fRN+LlXz6yLiHJ/UopPvf+OlaXZvH1x/fOWjzRLDKTlRi3XdGAbzLIt56yRmmPxXSet4PN9SW4F9iRIp5imUI4E2jTWh/WWvuAe4CrZzjuL4AHAGtlHVrYwJjX1jWyol3SuIyctCR+/cbs7RcAfvK7djoGPHzlqoaYZ7Cmy0pN4guXrmFnp5uH3uye12NYgWssVFvMzkEWwGfft4ri7FT+6ZHdU0Fve/8YP/ndEa7dVMmGqryEjSXJ6eArVzZydNDDTyyWu9cnfQsTYnlRJn94dg0PvdltetNorXUoLWSRnOftINKRwio102L5lqsAor85u8K3TVFKVQDXAHfOcH8NPKOUalVK3TLbkyilblFKtSilWvr6rPE/x0geX4AJf9C2zaGnS0t28uGNFTy1+/h7Tmz7jg/z0sE+ntt7gu8/f5D3ry3lgrriBT3fNRsr2FCVxzee2jdjbS07eGe50N4n4KzUJP7+knp2dLr5r22HeOlgH7c/sptkp+ILlyZ+K/X5dUV8oKGU7z/fxolhaywZeQOTjEwEpHxDglx5ehmTQW160+gx3yS+QFBmshIoLyOFDVV5bLPILtNYgqyZ1nOmr9F8F/iC1nqm+fnztNabCC03fkYpdeFMT6K1vktr3ay1bi4uXtgXsB0shmrv0914RjW+QJDP/mo7wxN+tNb84IU2Lv3uS/zRj1/nT37ewqTW/OMVaxf8XA6H4varGugb8fKDF9riMPrEc3l8OB2KnDT7l6u7dlMlG6vz+PbT+/mjH7/OtgN9/KWJrTb+8Yq1+CeD/GjbYVOef7qpz7vMZCXEhqp88jLMbxo9aPNq73a1ub6Et7qHpsqmmCmWs3sXEN1grBLomXZMM3BPOL+mCLhcKRXQWj+ste4B0Fr3KqUeIrT8+OKCR25zi6Ha+3QN5Tl867rT+dKDb3P9f71KY3kOD77ZzdUbyqeaAFfmZ7AsNz5fvBur8/nIpgp+/NIRbjyjytDEaiMMjvnJz0i2dAHNWDkcil/92dns6h4CID3FSUOZeTV0agoz+UBDKQ/v6OaLl60hJcncjdSL8aLKykJNo4vZur+PYFDjcJjzGRv02Lvau11dtLqY7zx7gJcO9vPhjRWnvoOBYjnzvAHUKaWWK6VSgBuBR6IP0Fov11rXaq1rgfuBT2utH1ZKZSqlsgGUUpnAB4FdcX0FNjUVZC2yD98NzVX8/JNn0uMe58E3u/nL963iux/dQHNtAc21BXELsCK+eOkakp2Krz++N66Pmwhum1Z7n01asnPqfW4szzU9eLyuqZLBMR8vWGDZoH800hxaZrISZUt9Mf2j5jaNfqfau7zvibSuIpfCzBRLFKY9ZZCltQ4AnyW0a3AvcK/WerdS6lal1K2nuHsp8Dul1E7gdeBxrfVTCx30YvBOS53F8yUbcd6qIh79i/P5vz85i7/5YL2hX7YlOWl85n2reHbPCV46aK9cvsExHwWLKMiymgvriinOTuW+ltjqtxkpUk6gNEe+bBPlwnDT6Bf2mfdFO9UcWj7nCRUq5VDMiwf7Td+BHtMcutb6Ca31aq31Sq31HeHb7tRavyfRXWv9ca31/eG/H9Zarw//aYzcVyyOflYnU1uUyfl1RQl5rj85fzk1hRl8/bG9lixCORu3x2/rGllWl+R08JGNFbywv9f0NhvHh0LPX5JtTo7aUlSUlcr6ylxTC1Mu1hULO9hcX8zgmI+3wikMZpGK7yYZGPOR4nScstK5OLXUJCef3bKK/SdGaO2wT4HSQY/P9uUbrO66pkomg5rf7DC31Mfx4QkKM1NMzw1bai6qL+HNoy7c4dyoRBsc85GS5CDTxv1p7eqCumKUgm0mb35Ycp94rTX3vH6UR3ZOz91PrMHRUIE6s/NWFosrTi8jM8VpiaWhWGitcXt8ti/fYHV1pdmsr8rjvpYuU2c5TwxPUGrSTsulbEt9MUENLx7sN+X5IzWy5DyfeAWZKZxemcfWA+bmZS25IEspxX2tXfzncwdNPen2jngpypYv2HjJSEni8nVlPPZWDx6f9etmjXoD+Cc1+RmyXGi0Pziziv0nRnjGxFY7x4cm4r7pQ5za6ZV55Gcks9WkvCyp9m6uzauL6RjwmNr9YckFWQDXN1XS1jvKm9PagCRSp8tDVX6Gac+/GF3fXMWYb5Kndh03eyintFiqvdvBtZsqw6129ph2su0dkZksMzgdiotWF7PtQJ8pCdDSHNpct1y4gte/dDFpyeYt1y7JIOuK08tITzZvaUlrTbdrnMr8dFOef7E6ozafmsIMWywZLoa+hXaR5HRw+1WNdA6O8+PfJb7Vji8QpH/UJzsLTbK5voSBMR9vm5AAPbiIWqfZUWZqEknzbN8WL0syyMpOS+bydWU8urOHcV/ir2z7Rrx4A0GqCmQmK56UUly3qZJXDw/QOegxezgnNbhIWurYxXmririksZQfvNDG8aHEttqJlG9YJjNZprhwdSgB2ox6aa4xv3zGl7glGWQBXN9cyag3wJO7jiX8uTtdoQBAlgvj7yNNlSgFv3itw+yhnJR7aiZLcrIS5bbLGwhMar711L6EPm+kf2Kp5GSZoiAzhfWVeQlvseMNTDLqDchM1hK3ZIOss5YXUFOYwb0tnac+OM66XOMAVBXIcmG8VeSlc82GCn72crulZ7MGwzlZkq+RONWFGfzpBct58M1uth9NXKmPSI0smckyz5b6EnZ2uRPay+6d1mmyTLyULdkgSynF9U2VvHZ4kKMDif0yjnz5V+TJTJYRPn9pPU6H4l+ftG6rHbfHh0NBTprMZCXSZ7asoiQ7la8+sjthidCRmSwJssyzub4YreGlBJZyGJDm0IIlHGQBXBteWrq/NbGzWZ2D4xRlpZIuBeoMUZabzq0XreSJt4/z+pFBs4czo8GxUN9CsxrXLlWZqUl88bI17Owa4sE3E1Og9MTwBClJDvJkadg0kV52iczLisxkSXPopW1JB1lluelcUFfM/a1dTCZwe2+nyyNLhQa75cIVlOWm8c+P7TG9d9VM3B6/fOma5MMbKthYncc3n9qXkJpqx4cnKM1JlYKUJnKESzm8eKAP/2QwIc/5znKhBFlL2ZIOsgBuaK6kZ2iCVw4lbhq5yzVOpSS9Gyo9xckXLl3D292Jm7GYC2kObR6HQ/GFS9fQN+Ll8beM3/hyfGhClgot4KoN5bg8fn75+6MJeb7IrtKiLMnJWsqWfJD1gYZS8jKSuTdBtZUmg5oe9zhVUiPLcB9aX876qjy+9dQ+xrzWqgLv8oSWC4U5zlpewPKiTO5vNf5zLy11rGHz6mLOWVHId397gKFxv+HP1zk4Tk5aErnpMmO9lC35ICs1ycmHN1Tw9O7jDHmM/+AdGxonENRSIysBHA7FV65soHfEy4+2HTJ7OO/i8vgoyJSTr1mUUlzXVMnvjxi78UVrzYlhrwRZFqCU4rYr1uIe9/PDF9oMf74ul0fO80KCLIDrmirxBYI8stP4ZaWp8g2yXJgQTTX5fGh9OT968TDd7nGzhwOEvnhdHr9UezfZNRsrQhtfths3mzU8EWDcPynLhRZxWkUuH9lYyU8TUOKlU7p6CCTIAkIfvIayHH7+agcBg5MiIx9s+fAlzhcuWwPAnVutMZvl8U3iCwSlErTJyvPSOX9VEQ+0dhm2OUIKkVrP5y+px+GAOw2c3dZah2ay5GJ6yZMgK+wvL66jrXeUuw1Oiux0jaNU6AQvEqMiL53N9cX8du8JtDZ/p+HUriOZyTLddU2VdLvHee3wgCGPH2nhIzNZ1rEsN40t9SU8t7fXsPNB/6iPCb+0ThMSZE25pLGU81YV8p1nD+AKfwkaoWvQQ1lOGilJ8r8+kbbUl3BsaIL9J0bMHgrucO6flHAw3yWNy8hOSzLs4uq4FCK1pC1rSjg+PMHeY8acDyKt02TFQsg3fZhSiq9c2cioN8B3nj1g2PN0ucaplKubhNtcXwLAC/sS279sJv3h1h5F2bK122xpyU4+dk4tj799jNaO+Beu7Q0HWSU58l5byebVxYBxTaMjaSEykyUkyIpSvyybPzyrmrt/38G+48OGPEenyyNXNyZYlptGQ1lOQis+zyZSP6dY6udYwqc2r6Q0J5WvPhr/wrXHhyfIy0gmLVm6O1hJSU4ap1XksNWg80Fkg5Oc64UEWdP89QdWk5OezFcf2RP39XpvYJLjwxOSDGmSLWuKae1wJaRGzsn0jYRmsoplJssSMlOT+IfL1vJW11DcdxoeH/LKUqFFbakvobXDhdsT//SQLpeHwswUMlKS4v7Ywl4kyJomLyOFv/3Aal49PMDTu4/H9bGPuSfQWqaQzbKlvoTJoOZ3CWwSO5PeES85aUkyu2EhV28oZ1N1Ht96aj8jE/ELwqUQqXVtWVNCUMOLBpwPOgclLUSESJA1g5vOrGbNsmy+/vheJvyTcXvcIwNjAFTLh88UG6ryyE1PNn3JsG/ES4l88VqKUorbr2qkf9TL9+NUqFJrTafLIzuJLWp9ZR75Gcls3Rf/80GofIO870KCrBklOR185coGulzj/M9Lh+P2uG0nRgGoK8mK22OK2CU5HVy4upit+/tMbRrdN+KVfCwLWl+Vx/VNlfzkd0c40j+24McbGPPh9vjl825RznDT6K0H4ns+mAxqut3jsmIhAAmyZnXuqiI+2FDKj7YdjttsVlvvKEVZKVKE0kTvX1tC/6iXZ/eeMG0MvSNeyceyqM9fWk+K08Edj+9Z8GMdjFxUlUqQZVXvbyhlcMwX19SQE8MT+Ce1JL0LIMYgSyl1qVJqv1KqTSn1xZMcd4ZSalIpdd1c72tFHz+vlhFvIG4fwIO9I6ySq1pTXbGujJXFmXzzyX34Da7uPxOtdWi5UIIsSyrJTuMvLq7jt3t72XZgYeU+2npDNZjqSrLjMTRhgEsbl1FXksU3n9qHLxCf84G0ThPRThlkKaWcwA+Ay4AG4CalVMMsx30TeHqu97Wqs5cXUpmfzv2tC99xpLXmYO+onHBNluR08KXL13K4f4y7X+tI+POPekO97GQmy7o+cV4ttYUZfPvpfQt6nIO9o2SlJlEqNbIsK8np4EtXrKV9wMP/xel8IDWyRLRYZrLOBNq01oe11j7gHuDqGY77C+ABoHce97Ukh0Nx7aZKftfWT88Cmwv3jngZmQjI0oEFvG9NCeeuLOT/PXcw4eUcIuUbpDildaUmOfnk+cvZ1T3M7p6heT/OwROjrCrJQikVx9GJeNu8upjzVxXxn88fZMiz8PNBp8sTbp0mm1tEbEFWBdAZ9XNX+LYpSqkK4Brgzrne1+qu3VSJ1vDgAuvnRPIzZLnQfEopbrtiLe5xv6FNYmfSG6mRlSUnYCv70PpyUpyOBc1ih2au5fNudUopvnT5WobG/fxw68J3lna5xinNTiM1SUq0iNiCrJkuw6Zvxfgu8AWt9fQM8VjuGzpQqVuUUi1KqZa+PvNbn0RUF2Zw1vIC7m/tWlBx0oOSn2EpjeW5XLymhEd29CS0abTMZNlDXkYKH2go5Tc7euaVq+P2+Ogf9crMtU00lOdw+boy7nmjc8EbnToHPVQVSNK7CIklyOoCqqJ+rgR6ph3TDNyjlGoHrgN+qJT6cIz3BUBrfZfWullr3VxcXBzb6BPk+uYq2gc8vNHumvdjHOwdJS8jmaIs2VloFRevLaXbPc7B3tGEPedUtXcp4WB51zVVMjjm4/l51FFq642Ua5GLKru48Ywqhsb9PLtnYTuPu1zjVErSuwiLJch6A6hTSi1XSqUANwKPRB+gtV6uta7VWtcC9wOf1lo/HMt97eCy05aRn5HMd397YN6zHm0nQksHkp9hHZvrw01iDShGOJveES/JTkVeRnLCnlPMzwV1RZRkp3J/a+epD54mErhLeoB9nLuyiIq8dO5bwBLx0LifnqFxVhRlxnFkws5OGWRprQPAZwntGtwL3Ku13q2UulUpdet87rvwYSdWZmoSf/OB1bxyaIBn5nGVo7XmQO8Iq+Sq1lLKctNZsyw7oRXgI4VIJdi2viSng2s2VfDC/r6pGchYHTwxSnqykwqp9m4bTofi2qZKXjrYN++NTm8edaE1NNXkx3l0wq5iqpOltX5Ca71aa71Sa31H+LY7tdbTE93RWn9ca33/ye5rRzedWU1dSRb/8sRevIG5rdlL5Wfr2rKmhJZ2F8Nx7Fd3Mr0jExRLSx3buL6pismgnnPnh0hNPIdDgmk7ub4ptNHpgXnOZm3vcOF0KNZX5cV3YMK2pOJ7jJKcDr58ZQMdAx5+9nL7nO4rlZ+t631rSggENS8nqGm0tNSxl1UlWVy7qZKfvtzO0QFPzPdrk52FtlRVkME5Kwq5r7VrXq12WjpcrC3LJjM1yYDRCTuSIGsOLlxdzMVrSvje821zWj6Qys/WtbEqj5y0pHklN89H/6i01LGbv7+0HqdD8a9P7o3p+JEJP8eGJlglF1W2dMMZlRwd9PB6++Cc7heYDLKj001TtSwVindIkDVHX7piLRP+Sb7z7P6Y7yOVn61rqml0nJvEziQwGWRgzCctdWymNCeNT21eyZO7jvPa4YFTHi87C+3t0sYyslOTuLdlbhse9h0fweObpKm2wKCRCTuSIGuOVhZn8bFza7nnjc6Yq0HvPTYslZ8t7H1rSugb8dJ6dP4lOmIxMOZDa2Qmy4b+7IIVlOem8c+P7WHyFMH43mOhmWvZWWhP6SlOrlxfzhNvH2NkDrmarR2h84ckvYtoEmTNw1++r4689GT++bE9pyzp0O0ep6XDxUWrrVX7S7zjg43LKMpK5VtP7TO0MGnvcLgQqQRZtpOe4uQLl61hd88wD5yi+8OjO3uoKcygtlBqJdnVDc2VTPiDPP7WsZjv09Lhoiw3TXaUineRIGsecjOS+ZsP1vPa4UGe3n3ykg4Pbe9C61BhQ2FNWeESHW+0u3h693HDnqdvdAKQmSy7+tD6cjZW5/Htp/cz6g3MeEznoIdXDw9w3aZKmbm2sQ1VedSVZM1pyXB7h4tNMoslppEga55uOqOK+tLsk5Z00Fpzf2sXZ68okI7sFndDcyWrS7P4xpP75tVGJRZTM1lSwsGWlFJ8+coG+ka83Ll15p6XD2zvQin4iFxU2ZpSihuaq9h+1D21celkjg2N0+0ep1mCLDGNBFnzlOR08I9XruXooIef/K59xmNaOly0D3i4rqlqxt8L60hyOviHy9fSPuDhF691GPIckR2p0lrJvjZV5/PhDeXc9dJhOgffXdIhGNQ8sL2L88KVw4W9fXhjBU6HiqkCvORjidlIkLUAF9QV8/61JfzghTZ6Rybe8/v7WjrJTHFy+bplJoxOzNXm1cWct6qQH207RGAy/rNZfaNe8jKSSU1yxv2xReL8/aVrSHYobvyP5zhw+jngcEBtLb//73vpHByX1IBFojg7lQ+sLeWXvz/KwOjJS/Y8v6+X7NQk1pblJGh0wi4kyFqgL12+Fm9gkq8/tpfWjsGpP68fGeTxt45x+boyMlKkMJ0dKKX4o7Nr6R3x8pIBxUl7h6UQ6WJQnpfOPdVufO4hrr34r3lkzQW0BtL52W93k+3QXNIoF1WLxd9dsppx3yT/9syBWY8ZnvDzxNvHuHJ9OclO+UoV7ybf/gu0ojiLj59by3+/dIRHdva85/c3nCFLhXbyvjUlFGamcG9LJ1vWlMT1sU+MTFAitdIWhXVf/yIPD3r4k+u+wl9+6O+nbv+DAy+RnnKliSMT8bSqJJs/PqeWn75yhD88u5rG8tz3HPPozh4m/EE+Kud6MQMJsuLgHy5by/vXluKdljCdnZbERqn+ayspSQ4+vLGC/321nYFRL4VxnHnqGPDILMdicfQoFVrz4P/9HdvL1xBUDhSaTT37gW+YPToRR391cR0P7+jmq4/u4de3nP2eXaP3vtFJfWk26yvfG4AJIXObceBwKM5aUciFq4vf9UcCLHu6obkK/6Tm4R3vnZmcr6FxP4NjPqmdtFhUVwOQ4fdyfsdOLmx/kwvad5BZFt/ZT2G+3Ixk/vaDq3n9yCD/8R8Pomtrp/Lw9v34V+zsGuKGM6qkZIeYkQRZQkxTvyx0VXpfS2fcipN2DIwBUFuUGZfHEya74w7ImBYwZ2SEbheLzo1nVPORPB//2ZvG3zVeg085oaODX9/3EslKc83GCrOHKCxKlguFmMF1zVV8+eFd7OoeZl0clgGO9IeCrOUSZC0ON98c+u9tt8HRo6GZrTvueOd2sag4HYp/v/Ovqa48l++efzOv1Kwn2+uhI28ZH+x4k4JMycMTM5MgS4gZfGh9OV9/bA/3tnTGJchq7/egFFRLUdrF4+abJahaQtTRo3yuo4O6/qM8vuYCtFLU9XfwF6/eC3zZ7OEJi5IgS4gZ5KYnc+lpy/jNjm5uu2ItackLq23VPjBGWU7agh9HCGGS6mro6OCK/S9zxf6X37m9psa8MQnLk5wsIWZxfVMVwxMBntlz8v6UsWgfGJN8LCHsTPLwxDxIkCXELM5dWUhFXjr3zaFJ7Gza+yXIEsLWbr4Z7rorNHOlVOi/d90lS8bipCTIEmIWDofiuqZKftfWT7d7fN6PM+Tx4/L4WV4oQZYQtnbzzdDeDsFg6L8SYIlTkCBLiJO4rqkSreGBGJrEzuZIuHxDjdTIEkKIJUWCLCFOoqogg3NXFnLP60eZ8E/O6zEiNbKkfIMQQiwtEmQJcQqf3bKKnqEJfvy7I/O6/5H+MZQKBWxCCCGWDgmyhDiFc1cVcWnjMn7wQhvHhybmfP/2/jHKc9OlfIMQQiwxEmQJEYMvXb6WQFDzzaf2zfm+RwY81BbJLJYQQiw1EmQJEYPqwgz+7ILlPPRmN9uPuuZ0346BMWplZ6EQQiw5MQVZSqlLlVL7lVJtSqkvzvD7q5VSbymldiilWpRS50f9rl0p9Xbkd/EcvBCJ9OnNq8hNT+anL7fHfB+3x4fb45ekdyGEWIJO2VZHKeUEfgB8AOgC3lBKPaK13hN12HPAI1prrZQ6HbgXWBP1+y1a6/44jluIhMtMTeKajRX88vWjuD0+8jJSTnmfSGPoGpnJEkKIJSeWmawzgTat9WGttQ+4B7g6+gCt9ajWWod/zAQ0QixCNzRX4QsEefjN7piO39UzDEBdSZaRwxJCCGFBsQRZFUB0X5Gu8G3vopS6Rim1D3gc+GTUrzTwjFKqVSl1y0IGK4TZGspzWFeRy69bunjnumJ22/b3UlWQLoVIhRBiCYolyFIz3Paebxet9UNa6zXAh4F/jvrVeVrrTcBlwGeUUhfO+CRK3RLO52rp6+uLYVhCmOOGM6rYe2yYXd3DJz3OG5jklUMDbF5dglIzfYyEEEIsZrEEWV1AVdTPlUDPbAdrrV8EViqlisI/94T/2ws8RGj5cab73aW1btZaNxcXF8c4fCES70Pry0lNcvDrlqMnPa6l3YXHN8lFq+XfsxBCLEWxBFlvAHVKqeVKqRTgRuCR6AOUUqtU+FJdKbUJSAEGlFKZSqns8O2ZwAeBXfF8AUIkWm56MpevK+M3b/bg9vhmPW7r/l5SnA7OXVWYwNEJIYSwilMGWVrrAPBZ4GlgL3Cv1nq3UupWpdSt4cOuBXYppXYQ2on40XAifCnwO6XUTuB14HGt9VMGvA4hEuqWC1cw5gvwvefbZj1m6/4+zlxeQEbKKTfxCiGEWIRiOvtrrZ8Anph2251Rf/8m8M0Z7ncYWL/AMQphOWvLcri+qYr/fbWdPzq7htppdbC63eMc7B3lo2dUzfIIQgghFjup+C7EPP3tB1eT7HTM2Gpn6/5eADbXSz6WEEIsVRJkCTFPJTlp/PmFK3ly13FePTTwrt89v7eXirx0VhZLfSwhhFiqJMgSYgH+7MLlVBWk86c/f4MX9vcyGdT882N7eG5fL1etL5fSDUIIsYRJRq4QC5CRksR9f34un/zZG/zpz1tYV5HLjk43Hz+3ls9fUm/28IQQQphIZrKEWKBluWncd+s5XLS6mLe63Nx+VQP/9KFGnA6ZxRJCiKVMZrKEiIPM1CT+54+b6R/1UpKTZvZwhBBCWIDMZAkRJw6HkgBLCCHEFAmyhBBCCCEMIEGWEEIIIYQBJMgSQgghhDCABFlCCCGEEAaQIEsIIYQQwgASZAkhhBBCGECCLCGEEEIIA0iQJYQQQghhAAmyhBBCCCEMIEGWEEIIIYQBlNba7DG8h1KqD+gw+GmKgH6Dn8PKlvLrX8qvHeT1y+tfuq9/Kb92kNdv5Ouv0VoXT7/RkkFWIiilWrTWzWaPwyxL+fUv5dcO8vrl9S/d17+UXzvI6zfj9ctyoRBCCCGEASTIEkIIIYQwwFIOsu4yewAmW8qvfym/dpDXL69/6VrKrx3k9Sf89S/ZnCwhhBBCCCMt5ZksIYQQQgjDLLkgSyl1qVJqv1KqTSn1RbPHYzSlVJVS6gWl1F6l1G6l1F+Fb/8npVS3UmpH+M/lZo/VKEqpdqXU2+HX2RK+rUAp9axS6mD4v/lmj9MISqn6qPd4h1JqWCn1ucX6/iulfqKU6lVK7Yq6bdb3Win1D+FzwX6l1CXmjDp+Znn931ZK7VNKvaWUekgplRe+vVYpNR71b+BO0wYeJ7O8/ln/rS+R9//XUa+9XSm1I3z7onr/T/JdZ+rnf0ktFyqlnMAB4ANAF/AGcJPWeo+pAzOQUqoMKNNab1dKZQOtwIeBG4BRrfW/mTm+RFBKtQPNWuv+qNu+BQxqrb8RDrbztdZfMGuMiRD+998NnAV8gkX4/iulLgRGgf/VWp8Wvm3G91op1QD8CjgTKAd+C6zWWk+aNPwFm+X1fxB4XmsdUEp9EyD8+muBxyLHLQazvP5/YoZ/60vl/Z/2+38HhrTWX1ts7/9Jvus+jomf/6U2k3Um0Ka1Pqy19gH3AFebPCZDaa2Paa23h/8+AuwFKswdlSVcDfw8/PefE/owLnYXA4e01kYX+jWN1vpFYHDazbO911cD92itvVrrI0AboXOEbc30+rXWz2itA+EfXwMqEz6wBJnl/Z/Nknj/I5RSitDF9a8SOqgEOcl3namf/6UWZFUAnVE/d7GEAo7wlctG4Pfhmz4bXkL4yWJdLgvTwDNKqVal1C3h20q11scg9OEESkwbXeLcyLtPsEvl/Z/tvV6K54NPAk9G/bxcKfWmUmqbUuoCswaVADP9W19q7/8FwAmt9cGo2xbl+z/tu87Uz/9SC7LUDLctifVSpVQW8ADwOa31MPBfwEpgA3AM+HfzRme487TWm4DLgM+Ep9SXFKVUCvAh4L7wTUvp/Z/NkjofKKVuAwLA3eGbjgHVWuuNwN8Av1RK5Zg1PgPN9m99Sb3/wE28+yJrUb7/M3zXzXroDLfF/f1fakFWF1AV9XMl0GPSWBJGKZVM6B/d3VrrBwG01ie01pNa6yDw39h8mvxktNY94f/2Ag8Req0nwmv4kbX8XvNGmBCXAdu11idgab3/zP5eL5nzgVLqY8CVwM06nIgbXiYZCP+9FTgErDZvlMY4yb/1pfT+JwEfAX4duW0xvv8zfddh8ud/qQVZbwB1Sqnl4Sv7G4FHTB6TocLr8D8G9mqtvxN1e1nUYdcAu6bfdzFQSmWGkyBRSmUCHyT0Wh8BPhY+7GPAb8wZYcK86yp2qbz/YbO9148ANyqlUpVSy4E64HUTxmcopdSlwBeAD2mtPVG3F4c3Q6CUWkHo9R82Z5TGOcm/9SXx/oe9H9inte6K3LDY3v/Zvusw+/OvtV5Sf4DLCe0wPATcZvZ4EvB6zyc0BfoWsCP853LgF8Db4dsfIbQrw/TxGvD6VwA7w392R95zoBB4DjgY/m+B2WM18P9BBjAA5Ebdtijff0KB5DHAT+hK9U9O9l4Dt4XPBfuBy8wev0Gvv41Q7knk839n+Nhrw5+JncB24Cqzx2/Q65/13/pSeP/Dt/8MuHXasYvq/T/Jd52pn/8lVcJBCCGEECJRltpyoRBCCCFEQkiQJYQQQghhAAmyhBBCCCEMIEGWEEIIIYQBJMgSQgghhDCABFlCCCGEEAaQIEsIIYQQwgASZAkhhBBCGOD/A/fN6DX9OSjNAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ppg_seg = ppg[0:200]\n",
    "seg_peaks = get_peaks(ppg_seg, fs)\n",
    "seg_onset = get_onsets(ppg_seg, seg_peaks, fs)\n",
    "seg_dia, seg_dic = get_dia_dic(ppg_seg, seg_peaks, seg_onset, fs)\n",
    "plt.figure(figsize=(10,5))\n",
    "plt.plot(ppg_seg)\n",
    "plt.scatter(seg_onset, ppg_seg[seg_onset], color='red')\n",
    "plt.scatter(seg_dia, ppg_seg[seg_dia], color='green')\n",
    "plt.scatter(seg_dic, ppg_seg[seg_dic], color='purple')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "N2-57tgiCupC"
   },
   "source": [
    "## Run BP Estimation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "id": "s4eg3GIbC8l6"
   },
   "outputs": [],
   "source": [
    "def get_fids(ppg, fs):\n",
    "  pks = get_peaks(ppg, fs)\n",
    "  ons = get_onsets(ppg, pks, fs)\n",
    "  dia, dic = get_dia_dic(ppg, pks, ons, fs)\n",
    "  return pks, ons, dia, dic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "id": "vMLI2bu6DXlR"
   },
   "outputs": [],
   "source": [
    "def get_signal(rel_segment_no, label, start_sec, len_sec):\n",
    "  if label == 'ppg':\n",
    "    chan = 'Pleth'\n",
    "  elif label == 'bp':\n",
    "    chan = 'ABP'\n",
    "  rel_segment_name = segment_names[rel_segment_no]\n",
    "  rel_segment_dir = segment_dirs[rel_segment_no]\n",
    "\n",
    "  sig, meta = wfdb.rdsamp(record_name=rel_segment_name, \n",
    "                            pn_dir=rel_segment_dir, \n",
    "                            sampfrom=0, sampto=100)\n",
    "  fs = round(meta['fs'])\n",
    "\n",
    "  sampfrom = fs*start_sec\n",
    "  sampto = fs*(start_sec + len_sec)\n",
    "\n",
    "  req_sig, fields = wfdb.rdsamp(record_name=rel_segment_name, \n",
    "                            pn_dir=rel_segment_dir, \n",
    "                            sampfrom=sampfrom, sampto=sampto, \n",
    "                            channel_names = [chan]) \n",
    "  req_sig = req_sig.squeeze()\n",
    "\n",
    "  return req_sig, fs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "id": "G5zfp1JNEbGI"
   },
   "outputs": [],
   "source": [
    "def get_less_closest(array, value):\n",
    "  array = np.array(array)\n",
    "  index, arr = np.argwhere(array<value), array[array<value]\n",
    "  idx = (np.abs(arr - value)).argmin()\n",
    "  return array[index[idx]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "id": "gCPBmuxaH0MS"
   },
   "outputs": [],
   "source": [
    "def remove_nan(sig):\n",
    "    nan_ind= np.argwhere(np.isnan(sig)) \n",
    "    return np.delete(sig, nan_ind)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Y09r9gv1J1x8"
   },
   "source": [
    "#### Test Example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "SEpilXt1J3qY",
    "outputId": "4659795a-03f4-4e58-a584-a003f811283e"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-60, -18, -49, -31, -19, -16, -34, -14, -18, -20, -19, -17, -15,\n",
       "       -17, -19, -15, -23, -18, -15, -16, -14, -21, -25, -17, -19, -17,\n",
       "       -18, -16, -17, -12, -19, -15, -23, -15, -18, -20, -45, -16, -14,\n",
       "       -14, -17])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dias = [467,505,617,679,747,825,923,982,1065,1147,1225,1302,1378,1460,1540,1616,1703,1776,1853,1933,2010,2096,2179,2250,2331,2407,2487,2563,2642,2716,2800,2875,2961,3031,3111,3190,3292,3342,3418,3496,3576]\n",
    "pks = [326,407,487,568,648,728,809,889,968,1047,1127,1206,1285,1363,1443,1521,1601,1680,1758,1838,1917,1996,2075,2154,2233,2312,2390,2469,2547,2625,2704,2781,2860,2938,3016,3093,3170,3247,3326,3404,3482,3559,3636]\n",
    "\n",
    "peaks = [get_less_closest(pks, dia)[0] for dia in dias]\n",
    "delta_t = np.subtract(peaks, dias)\n",
    "\n",
    "delta_t"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "EmJGYc3OKLO8"
   },
   "source": [
    "### Get delta_t and bp example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "id": "GSWmBTnFG2X4"
   },
   "outputs": [],
   "source": [
    "ppg, fs = get_signal(3, 'ppg', 100, 60)\n",
    "ppg = remove_nan(ppg)\n",
    "pks, ons, dias, dics = get_fids(ppg, fs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "id": "VxakNbP3G4L7"
   },
   "outputs": [],
   "source": [
    "corrs_peaks = [get_less_closest(pks, dia)[0] for dia in dias]\n",
    "delta_t = np.subtract(dias, corrs_peaks)/fs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "6o2MuGapKkpG",
    "outputId": "dc60af03-1059-44c5-91b6-c2fd552dc87a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.3548387096774194 118.84375 64.0625\n"
     ]
    }
   ],
   "source": [
    "bp, fs = get_signal(3, 'bp', 100, 60)\n",
    "bp = remove_nan(bp)\n",
    "pks = get_peaks(bp, fs)\n",
    "ons = get_onsets(bp, pks, fs)\n",
    "\n",
    "sbp = np.median(bp[pks])\n",
    "dbp = np.median(bp[ons])\n",
    "\n",
    "print(np.mean(delta_t), sbp, dbp)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "OJfzREAFKcH_"
   },
   "source": [
    "\n",
    "### Get training set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "1r0pjOopRLaR",
    "outputId": "6950a9a9-bddc-47dc-f53c-db99dc9712df"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "52"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(segment_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "id": "9yaI7x32NGAA"
   },
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "id": "cPoN8YfArbBh"
   },
   "outputs": [],
   "source": [
    "train_set = pd.DataFrame(np.nan, \n",
    "                         columns = ['id', \n",
    "                                    'pk_to_dia', 'pk_to_dic', \n",
    "                                    'on_to_pk', 'on_to_dia', 'on_to_dic',\n",
    "                                    'peak_len', \n",
    "                                    'peak_h', 'dia_h', 'dic_h', 'on_h',\n",
    "                                    'sbp', 'dbp'], \n",
    "                         index = list(range(0,100)))\n",
    "train_set['id'] = train_set['id'].astype(str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "id": "MjUQq1LMJ1XK"
   },
   "outputs": [
    {
     "ename": "FileNotFoundError",
     "evalue": "[Errno 2] No such file or directory: 'res.csv'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-37-82b76696f767>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtrain_set\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'res.csv'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Unnamed: 0'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mtrain_set\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtrain_set\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdropna\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.8/site-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36mread_csv\u001b[0;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, dialect, error_bad_lines, warn_bad_lines, delim_whitespace, low_memory, memory_map, float_precision, storage_options)\u001b[0m\n\u001b[1;32m    608\u001b[0m     \u001b[0mkwds\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkwds_defaults\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    609\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 610\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0m_read\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    611\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    612\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.8/site-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m_read\u001b[0;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[1;32m    460\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    461\u001b[0m     \u001b[0;31m# Create the parser.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 462\u001b[0;31m     \u001b[0mparser\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mTextFileReader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    463\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    464\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mchunksize\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0miterator\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.8/site-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[1;32m    817\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"has_index_names\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"has_index_names\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    818\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 819\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_make_engine\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mengine\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    820\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    821\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.8/site-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m_make_engine\u001b[0;34m(self, engine)\u001b[0m\n\u001b[1;32m   1048\u001b[0m             )\n\u001b[1;32m   1049\u001b[0m         \u001b[0;31m# error: Too many arguments for \"ParserBase\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1050\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mmapping\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mengine\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# type: ignore[call-arg]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1051\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1052\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_failover_to_python\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.8/site-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, src, **kwds)\u001b[0m\n\u001b[1;32m   1865\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1866\u001b[0m         \u001b[0;31m# open handles\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1867\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_open_handles\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msrc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1868\u001b[0m         \u001b[0;32massert\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhandles\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1869\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mkey\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m\"storage_options\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"encoding\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"memory_map\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"compression\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.8/site-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m_open_handles\u001b[0;34m(self, src, kwds)\u001b[0m\n\u001b[1;32m   1360\u001b[0m         \u001b[0mLet\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mreaders\u001b[0m \u001b[0mopen\u001b[0m \u001b[0mIOHanldes\u001b[0m \u001b[0mafter\u001b[0m \u001b[0mthey\u001b[0m \u001b[0mare\u001b[0m \u001b[0mdone\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mtheir\u001b[0m \u001b[0mpotential\u001b[0m \u001b[0mraises\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1361\u001b[0m         \"\"\"\n\u001b[0;32m-> 1362\u001b[0;31m         self.handles = get_handle(\n\u001b[0m\u001b[1;32m   1363\u001b[0m             \u001b[0msrc\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1364\u001b[0m             \u001b[0;34m\"r\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.8/site-packages/pandas/io/common.py\u001b[0m in \u001b[0;36mget_handle\u001b[0;34m(path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_options)\u001b[0m\n\u001b[1;32m    640\u001b[0m                 \u001b[0merrors\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"replace\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    641\u001b[0m             \u001b[0;31m# Encoding\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 642\u001b[0;31m             handle = open(\n\u001b[0m\u001b[1;32m    643\u001b[0m                 \u001b[0mhandle\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    644\u001b[0m                 \u001b[0mioargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmode\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'res.csv'"
     ]
    }
   ],
   "source": [
    "train_set = pd.read_csv('res.csv').set_index('Unnamed: 0')\n",
    "train_set = train_set.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "n0lCScijkPAT",
    "outputId": "6a1d9d33-d100-448a-cd6e-d552466e51f6"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(370, 5)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_set.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "id": "Ud5wlDBmC0XF"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Curr start time:40\n",
      "Curr subj no:40\n",
      " - getting PPG data\n",
      " - removing nans from PPG\n",
      " - analysing PPG\n",
      " - refining PPG analyses\n",
      " - getting BP data\n",
      " - removing nans from BP\n",
      " - analysing BP\n",
      " - storing data\n",
      "Curr subj no:41\n",
      " - getting PPG data\n",
      " - removing nans from PPG\n",
      " - analysing PPG\n",
      " - refining PPG analyses\n",
      " - getting BP data\n",
      " - removing nans from BP\n",
      " - analysing BP\n",
      " - storing data\n",
      "Curr subj no:42\n",
      " - getting PPG data\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-44-099a594433ac>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      6\u001b[0m         \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Curr subj no:{}'\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mno\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m         \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m' - getting PPG data'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 8\u001b[0;31m         \u001b[0mppg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_signal\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mno\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'ppg'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstart\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mintv\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      9\u001b[0m         \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m' - removing nans from PPG'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m         \u001b[0mppg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mremove_nan\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mppg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-27-16c0e47ffdf6>\u001b[0m in \u001b[0;36mget_signal\u001b[0;34m(rel_segment_no, label, start_sec, len_sec)\u001b[0m\n\u001b[1;32m      7\u001b[0m   \u001b[0mrel_segment_dir\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msegment_dirs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mrel_segment_no\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 9\u001b[0;31m   sig, meta = wfdb.rdsamp(record_name=rel_segment_name, \n\u001b[0m\u001b[1;32m     10\u001b[0m                             \u001b[0mpn_dir\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mrel_segment_dir\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m                             sampfrom=0, sampto=100)\n",
      "\u001b[0;32m~/anaconda3/lib/python3.8/site-packages/wfdb/io/record.py\u001b[0m in \u001b[0;36mrdsamp\u001b[0;34m(record_name, sampfrom, sampto, channels, pn_dir, channel_names, warn_empty, return_res)\u001b[0m\n\u001b[1;32m   2320\u001b[0m         )\n\u001b[1;32m   2321\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2322\u001b[0;31m     record = rdrecord(\n\u001b[0m\u001b[1;32m   2323\u001b[0m         \u001b[0mrecord_name\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mrecord_name\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2324\u001b[0m         \u001b[0msampfrom\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msampfrom\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.8/site-packages/wfdb/io/record.py\u001b[0m in \u001b[0;36mrdrecord\u001b[0;34m(record_name, sampfrom, sampto, channels, physical, pn_dir, m2s, smooth_frames, ignore_skew, return_res, force_channels, channel_names, warn_empty)\u001b[0m\n\u001b[1;32m   2116\u001b[0m         \u001b[0msig_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2117\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2118\u001b[0;31m         record.e_d_signal = _signal._rd_segment(\n\u001b[0m\u001b[1;32m   2119\u001b[0m             \u001b[0mfile_name\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mrecord\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfile_name\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2120\u001b[0m             \u001b[0mdir_name\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdir_name\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.8/site-packages/wfdb/io/_signal.py\u001b[0m in \u001b[0;36m_rd_segment\u001b[0;34m(file_name, dir_name, pn_dir, fmt, n_sig, sig_len, byte_offset, samps_per_frame, skew, init_value, sampfrom, sampto, channels, ignore_skew, no_file, sig_data, return_res)\u001b[0m\n\u001b[1;32m   1205\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0mfn\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mw_file_name\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1206\u001b[0m         \u001b[0;31m# Get the list of all signals contained in the dat file\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1207\u001b[0;31m         datsignals = _rd_dat_signals(\n\u001b[0m\u001b[1;32m   1208\u001b[0m             \u001b[0mfile_name\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfn\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1209\u001b[0m             \u001b[0mdir_name\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdir_name\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.8/site-packages/wfdb/io/_signal.py\u001b[0m in \u001b[0;36m_rd_dat_signals\u001b[0;34m(file_name, dir_name, pn_dir, fmt, n_sig, sig_len, byte_offset, samps_per_frame, skew, init_value, sampfrom, sampto, no_file, sig_data)\u001b[0m\n\u001b[1;32m   1333\u001b[0m         \u001b[0mdata_to_read\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msig_data\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1334\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mfmt\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mCOMPRESSED_FMTS\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1335\u001b[0;31m         data_to_read = _rd_compressed_file(\n\u001b[0m\u001b[1;32m   1336\u001b[0m             \u001b[0mfile_name\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfile_name\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1337\u001b[0m             \u001b[0mdir_name\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdir_name\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.8/site-packages/wfdb/io/_signal.py\u001b[0m in \u001b[0;36m_rd_compressed_file\u001b[0;34m(file_name, dir_name, pn_dir, fmt, sample_offset, n_sig, samps_per_frame, start_frame, end_frame)\u001b[0m\n\u001b[1;32m   1843\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1844\u001b[0m     \u001b[0;32mwith\u001b[0m \u001b[0m_coreio\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_open_file\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpn_dir\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfile_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"rb\"\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mfp\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1845\u001b[0;31m         \u001b[0msignature\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m4\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1846\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0msignature\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;34mb\"fLaC\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1847\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"{fp.name} is not a FLAC file\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.8/site-packages/wfdb/io/_url.py\u001b[0m in \u001b[0;36mread\u001b[0;34m(self, size)\u001b[0m\n\u001b[1;32m    579\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"invalid size: %r\"\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0msize\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    580\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 581\u001b[0;31m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34mb\"\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_read_range\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstart\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mend\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    582\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_pos\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    583\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.8/site-packages/wfdb/io/_url.py\u001b[0m in \u001b[0;36m_read_range\u001b[0;34m(self, start, end)\u001b[0m\n\u001b[1;32m    472\u001b[0m                 \u001b[0mbuffer_store\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    473\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 474\u001b[0;31m         \u001b[0;32mwith\u001b[0m \u001b[0mRangeTransfer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_current_url\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreq_start\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreq_end\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mxfer\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    475\u001b[0m             \u001b[0;31m# Update current file URL.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    476\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_current_url\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mxfer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresponse_url\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.8/site-packages/wfdb/io/_url.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, url, start, end)\u001b[0m\n\u001b[1;32m    161\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    162\u001b[0m         \u001b[0msession\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_get_session\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 163\u001b[0;31m         self._response = session.request(\n\u001b[0m\u001b[1;32m    164\u001b[0m             \u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mheaders\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mheaders\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstream\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    165\u001b[0m         )\n",
      "\u001b[0;32m~/anaconda3/lib/python3.8/site-packages/requests/sessions.py\u001b[0m in \u001b[0;36mrequest\u001b[0;34m(self, method, url, params, data, headers, cookies, files, auth, timeout, allow_redirects, proxies, hooks, stream, verify, cert, json)\u001b[0m\n\u001b[1;32m    540\u001b[0m         }\n\u001b[1;32m    541\u001b[0m         \u001b[0msend_kwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msettings\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 542\u001b[0;31m         \u001b[0mresp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprep\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0msend_kwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    543\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    544\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mresp\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.8/site-packages/requests/sessions.py\u001b[0m in \u001b[0;36msend\u001b[0;34m(self, request, **kwargs)\u001b[0m\n\u001b[1;32m    653\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    654\u001b[0m         \u001b[0;31m# Send the request\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 655\u001b[0;31m         \u001b[0mr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0madapter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrequest\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    656\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    657\u001b[0m         \u001b[0;31m# Total elapsed time of the request (approximately)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.8/site-packages/requests/adapters.py\u001b[0m in \u001b[0;36msend\u001b[0;34m(self, request, stream, timeout, verify, cert, proxies)\u001b[0m\n\u001b[1;32m    437\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    438\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mchunked\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 439\u001b[0;31m                 resp = conn.urlopen(\n\u001b[0m\u001b[1;32m    440\u001b[0m                     \u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mrequest\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    441\u001b[0m                     \u001b[0murl\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.8/site-packages/urllib3/connectionpool.py\u001b[0m in \u001b[0;36murlopen\u001b[0;34m(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, **response_kw)\u001b[0m\n\u001b[1;32m    697\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    698\u001b[0m             \u001b[0;31m# Make the request on the httplib connection object.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 699\u001b[0;31m             httplib_response = self._make_request(\n\u001b[0m\u001b[1;32m    700\u001b[0m                 \u001b[0mconn\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    701\u001b[0m                 \u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.8/site-packages/urllib3/connectionpool.py\u001b[0m in \u001b[0;36m_make_request\u001b[0;34m(self, conn, method, url, timeout, chunked, **httplib_request_kw)\u001b[0m\n\u001b[1;32m    443\u001b[0m                     \u001b[0;31m# Python 3 (including for exceptions like SystemExit).\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    444\u001b[0m                     \u001b[0;31m# Otherwise it looks like a bug in the code.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 445\u001b[0;31m                     \u001b[0msix\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mraise_from\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    446\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mSocketTimeout\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mBaseSSLError\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mSocketError\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    447\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_raise_timeout\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0merr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0murl\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimeout_value\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mread_timeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.8/site-packages/urllib3/packages/six.py\u001b[0m in \u001b[0;36mraise_from\u001b[0;34m(value, from_value)\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.8/site-packages/urllib3/connectionpool.py\u001b[0m in \u001b[0;36m_make_request\u001b[0;34m(self, conn, method, url, timeout, chunked, **httplib_request_kw)\u001b[0m\n\u001b[1;32m    438\u001b[0m                 \u001b[0;31m# Python 3\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    439\u001b[0m                 \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 440\u001b[0;31m                     \u001b[0mhttplib_response\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mconn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgetresponse\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    441\u001b[0m                 \u001b[0;32mexcept\u001b[0m \u001b[0mBaseException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    442\u001b[0m                     \u001b[0;31m# Remove the TypeError from the exception chain in\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.8/http/client.py\u001b[0m in \u001b[0;36mgetresponse\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1345\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1346\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1347\u001b[0;31m                 \u001b[0mresponse\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbegin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1348\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mConnectionError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1349\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.8/http/client.py\u001b[0m in \u001b[0;36mbegin\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    305\u001b[0m         \u001b[0;31m# read until we get a non-100 response\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    306\u001b[0m         \u001b[0;32mwhile\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 307\u001b[0;31m             \u001b[0mversion\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstatus\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreason\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_read_status\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    308\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mstatus\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mCONTINUE\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    309\u001b[0m                 \u001b[0;32mbreak\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.8/http/client.py\u001b[0m in \u001b[0;36m_read_status\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    266\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    267\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_read_status\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 268\u001b[0;31m         \u001b[0mline\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreadline\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_MAXLINE\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"iso-8859-1\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    269\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mline\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0m_MAXLINE\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    270\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mLineTooLong\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"status line\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.8/socket.py\u001b[0m in \u001b[0;36mreadinto\u001b[0;34m(self, b)\u001b[0m\n\u001b[1;32m    667\u001b[0m         \u001b[0;32mwhile\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    668\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 669\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_sock\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrecv_into\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    670\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    671\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_timeout_occurred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.8/ssl.py\u001b[0m in \u001b[0;36mrecv_into\u001b[0;34m(self, buffer, nbytes, flags)\u001b[0m\n\u001b[1;32m   1239\u001b[0m                   \u001b[0;34m\"non-zero flags not allowed in calls to recv_into() on %s\"\u001b[0m \u001b[0;34m%\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1240\u001b[0m                   self.__class__)\n\u001b[0;32m-> 1241\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnbytes\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbuffer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1242\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1243\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrecv_into\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbuffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnbytes\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mflags\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.8/ssl.py\u001b[0m in \u001b[0;36mread\u001b[0;34m(self, len, buffer)\u001b[0m\n\u001b[1;32m   1097\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1098\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mbuffer\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1099\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_sslobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbuffer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1100\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1101\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_sslobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "intv = 60\n",
    "i = 0\n",
    "for start in range(40, 800, 60):\n",
    "    print('Curr start time:{}'.format(start))\n",
    "    for no in range(40, 52):\n",
    "        print('Curr subj no:{}'.format(no))\n",
    "        print(' - getting PPG data')\n",
    "        ppg, fs = get_signal(no, 'ppg', start, intv)\n",
    "        print(' - removing nans from PPG')\n",
    "        ppg = remove_nan(ppg)\n",
    "        print(' - analysing PPG')\n",
    "        pks, ons, dias, dics = get_fids(ppg, fs)\n",
    "        print(' - refining PPG analyses')\n",
    "        on_pks = [get_less_closest(ons, pk)[0] for pk in pks[1:]]\n",
    "        on_to_p = np.subtract(pks[1:], on_pks)/fs\n",
    "        on_dia = [get_less_closest(ons, dia)[0] for dia in dias]\n",
    "        on_to_dia = np.subtract(dias, on_dia)/fs\n",
    "        on_dic = [get_less_closest(ons, dic)[0] for dic in dics]\n",
    "        on_to_dic = np.subtract(dics, on_dic)/fs\n",
    "\n",
    "        dia_pks = [get_less_closest(pks, dia)[0] for dia in dias]\n",
    "        p_to_dia = np.subtract(dias, dia_pks)/fs\n",
    "        dic_pks = [get_less_closest(pks, dic)[0] for dic in dics]\n",
    "        p_to_dic = np.subtract(dics, dic_pks)/fs\n",
    "        print(' - getting BP data')\n",
    "        bp, fs = get_signal(no, 'bp', start, intv)\n",
    "        print(' - removing nans from BP')\n",
    "        bp = remove_nan(bp)\n",
    "        print(' - analysing BP')\n",
    "        pks = get_peaks(bp, fs)\n",
    "        ons = get_onsets(bp, pks, fs)\n",
    "\n",
    "        sbp = np.median(bp[pks])\n",
    "        dbp = np.median(bp[ons])\n",
    "\n",
    "        print(' - storing data')\n",
    "        train_set.at[i, 'id'] = str(no)+'-'+str(start)+'-'+str(intv)\n",
    "        train_set.at[i, 'on_to_pk'] = np.mean(on_to_p)\n",
    "        train_set.at[i, 'on_to_dia'] = np.mean(on_to_dia)\n",
    "        train_set.at[i, 'on_to_dic'] = np.mean(on_to_dic)\n",
    "\n",
    "        train_set.at[i, 'pk_to_dia'] = np.mean(p_to_dia)\n",
    "        train_set.at[i, 'pk_to_dic'] = np.mean(p_to_dic)\n",
    "\n",
    "        train_set.at[i, 'peak_h'] = np.mean(ppg[pks])\n",
    "        train_set.at[i, 'dia_h'] = np.mean(ppg[dias])\n",
    "        train_set.at[i, 'dic_h'] = np.mean(ppg[dics])\n",
    "        train_set.at[i, 'on_h'] = np.mean(ppg[ons])\n",
    "\n",
    "        train_set.at[i, 'peak_len'] = np.mean(np.diff(pks)/fs)\n",
    "        train_set.at[i, 'sbp'] = sbp\n",
    "        train_set.at[i, 'dbp'] = dbp\n",
    "\n",
    "        i += 1\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 884
    },
    "id": "_S9u5m53QVP4",
    "outputId": "5a567891-ddb4-4826-ad0d-6ccccd6a7249"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "  <div id=\"df-55325c22-14bb-41de-9f8b-d509e83a3564\">\n",
       "    <div class=\"colab-df-container\">\n",
       "      <div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>pk_to_dia</th>\n",
       "      <th>pk_to_dic</th>\n",
       "      <th>on_to_pk</th>\n",
       "      <th>on_to_dia</th>\n",
       "      <th>on_to_dic</th>\n",
       "      <th>peak_len</th>\n",
       "      <th>peak_h</th>\n",
       "      <th>dia_h</th>\n",
       "      <th>dic_h</th>\n",
       "      <th>on_h</th>\n",
       "      <th>sbp</th>\n",
       "      <th>dbp</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1-40-60</td>\n",
       "      <td>0.209677</td>\n",
       "      <td>0.161290</td>\n",
       "      <td>0.358781</td>\n",
       "      <td>0.483871</td>\n",
       "      <td>0.435484</td>\n",
       "      <td>1.282258</td>\n",
       "      <td>0.289317</td>\n",
       "      <td>0.491211</td>\n",
       "      <td>0.546875</td>\n",
       "      <td>0.297408</td>\n",
       "      <td>113.75000</td>\n",
       "      <td>40.90625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3-40-60</td>\n",
       "      <td>0.338710</td>\n",
       "      <td>0.225806</td>\n",
       "      <td>0.184612</td>\n",
       "      <td>0.516129</td>\n",
       "      <td>0.403226</td>\n",
       "      <td>0.794246</td>\n",
       "      <td>0.412357</td>\n",
       "      <td>0.544922</td>\n",
       "      <td>0.515869</td>\n",
       "      <td>0.480327</td>\n",
       "      <td>118.00000</td>\n",
       "      <td>63.71875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4-40-60</td>\n",
       "      <td>0.209677</td>\n",
       "      <td>0.161290</td>\n",
       "      <td>0.188054</td>\n",
       "      <td>0.354839</td>\n",
       "      <td>0.306452</td>\n",
       "      <td>2.086849</td>\n",
       "      <td>0.420392</td>\n",
       "      <td>0.480225</td>\n",
       "      <td>0.493652</td>\n",
       "      <td>0.469651</td>\n",
       "      <td>91.81250</td>\n",
       "      <td>68.40625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6-40-60</td>\n",
       "      <td>0.161290</td>\n",
       "      <td>0.064516</td>\n",
       "      <td>0.696885</td>\n",
       "      <td>2.225806</td>\n",
       "      <td>2.129032</td>\n",
       "      <td>0.641616</td>\n",
       "      <td>0.455235</td>\n",
       "      <td>0.586426</td>\n",
       "      <td>0.627197</td>\n",
       "      <td>0.498803</td>\n",
       "      <td>85.06250</td>\n",
       "      <td>52.68750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7-40-60</td>\n",
       "      <td>0.241935</td>\n",
       "      <td>0.112903</td>\n",
       "      <td>0.206452</td>\n",
       "      <td>0.451613</td>\n",
       "      <td>0.322581</td>\n",
       "      <td>0.723019</td>\n",
       "      <td>0.404934</td>\n",
       "      <td>0.451660</td>\n",
       "      <td>0.459473</td>\n",
       "      <td>0.481081</td>\n",
       "      <td>102.93750</td>\n",
       "      <td>53.75000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>394</th>\n",
       "      <td>5-640-60</td>\n",
       "      <td>0.258065</td>\n",
       "      <td>0.225806</td>\n",
       "      <td>0.218203</td>\n",
       "      <td>0.483871</td>\n",
       "      <td>0.451613</td>\n",
       "      <td>0.783871</td>\n",
       "      <td>0.371592</td>\n",
       "      <td>0.559326</td>\n",
       "      <td>0.590576</td>\n",
       "      <td>0.469294</td>\n",
       "      <td>93.06250</td>\n",
       "      <td>37.68750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>395</th>\n",
       "      <td>6-640-60</td>\n",
       "      <td>0.838710</td>\n",
       "      <td>0.774194</td>\n",
       "      <td>0.398185</td>\n",
       "      <td>1.032258</td>\n",
       "      <td>0.967742</td>\n",
       "      <td>1.217570</td>\n",
       "      <td>0.423503</td>\n",
       "      <td>0.329834</td>\n",
       "      <td>0.353027</td>\n",
       "      <td>0.418488</td>\n",
       "      <td>115.21875</td>\n",
       "      <td>61.18750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>396</th>\n",
       "      <td>7-640-60</td>\n",
       "      <td>0.145161</td>\n",
       "      <td>0.112903</td>\n",
       "      <td>0.194556</td>\n",
       "      <td>0.322581</td>\n",
       "      <td>0.290323</td>\n",
       "      <td>0.734274</td>\n",
       "      <td>0.349453</td>\n",
       "      <td>0.499023</td>\n",
       "      <td>0.541260</td>\n",
       "      <td>0.400525</td>\n",
       "      <td>89.12500</td>\n",
       "      <td>46.87500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>397</th>\n",
       "      <td>8-640-60</td>\n",
       "      <td>0.161290</td>\n",
       "      <td>0.112903</td>\n",
       "      <td>0.190223</td>\n",
       "      <td>0.354839</td>\n",
       "      <td>0.306452</td>\n",
       "      <td>0.610383</td>\n",
       "      <td>0.389623</td>\n",
       "      <td>0.490967</td>\n",
       "      <td>0.526611</td>\n",
       "      <td>0.501600</td>\n",
       "      <td>152.75000</td>\n",
       "      <td>59.68750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>398</th>\n",
       "      <td>9-640-60</td>\n",
       "      <td>0.274194</td>\n",
       "      <td>0.145161</td>\n",
       "      <td>0.209126</td>\n",
       "      <td>0.403226</td>\n",
       "      <td>0.274194</td>\n",
       "      <td>0.479544</td>\n",
       "      <td>0.409711</td>\n",
       "      <td>0.369873</td>\n",
       "      <td>0.361816</td>\n",
       "      <td>0.464401</td>\n",
       "      <td>97.81250</td>\n",
       "      <td>53.81250</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>370 rows × 13 columns</p>\n",
       "</div>\n",
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       "  "
      ],
      "text/plain": [
       "                  id  pk_to_dia  pk_to_dic  on_to_pk  on_to_dia  on_to_dic  \\\n",
       "Unnamed: 0                                                                   \n",
       "0            1-40-60   0.209677   0.161290  0.358781   0.483871   0.435484   \n",
       "2            3-40-60   0.338710   0.225806  0.184612   0.516129   0.403226   \n",
       "3            4-40-60   0.209677   0.161290  0.188054   0.354839   0.306452   \n",
       "5            6-40-60   0.161290   0.064516  0.696885   2.225806   2.129032   \n",
       "6            7-40-60   0.241935   0.112903  0.206452   0.451613   0.322581   \n",
       "...              ...        ...        ...       ...        ...        ...   \n",
       "394         5-640-60   0.258065   0.225806  0.218203   0.483871   0.451613   \n",
       "395         6-640-60   0.838710   0.774194  0.398185   1.032258   0.967742   \n",
       "396         7-640-60   0.145161   0.112903  0.194556   0.322581   0.290323   \n",
       "397         8-640-60   0.161290   0.112903  0.190223   0.354839   0.306452   \n",
       "398         9-640-60   0.274194   0.145161  0.209126   0.403226   0.274194   \n",
       "\n",
       "            peak_len    peak_h     dia_h     dic_h      on_h        sbp  \\\n",
       "Unnamed: 0                                                                \n",
       "0           1.282258  0.289317  0.491211  0.546875  0.297408  113.75000   \n",
       "2           0.794246  0.412357  0.544922  0.515869  0.480327  118.00000   \n",
       "3           2.086849  0.420392  0.480225  0.493652  0.469651   91.81250   \n",
       "5           0.641616  0.455235  0.586426  0.627197  0.498803   85.06250   \n",
       "6           0.723019  0.404934  0.451660  0.459473  0.481081  102.93750   \n",
       "...              ...       ...       ...       ...       ...        ...   \n",
       "394         0.783871  0.371592  0.559326  0.590576  0.469294   93.06250   \n",
       "395         1.217570  0.423503  0.329834  0.353027  0.418488  115.21875   \n",
       "396         0.734274  0.349453  0.499023  0.541260  0.400525   89.12500   \n",
       "397         0.610383  0.389623  0.490967  0.526611  0.501600  152.75000   \n",
       "398         0.479544  0.409711  0.369873  0.361816  0.464401   97.81250   \n",
       "\n",
       "                 dbp  \n",
       "Unnamed: 0            \n",
       "0           40.90625  \n",
       "2           63.71875  \n",
       "3           68.40625  \n",
       "5           52.68750  \n",
       "6           53.75000  \n",
       "...              ...  \n",
       "394         37.68750  \n",
       "395         61.18750  \n",
       "396         46.87500  \n",
       "397         59.68750  \n",
       "398         53.81250  \n",
       "\n",
       "[370 rows x 13 columns]"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "vFdf_43zSuKw"
   },
   "outputs": [],
   "source": [
    "train_set.to_csv('train_res.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "tGKtQfuu-y0_"
   },
   "outputs": [],
   "source": [
    "train_set.to_csv('/content/drive/MyDrive/ppg/test.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "eBlAs_IzYNOg"
   },
   "source": [
    "## Run Linear Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "zL9xEWPDWEtB"
   },
   "outputs": [],
   "source": [
    "train_set = pd.read_csv('/content/drive/MyDrive/ppg/train.csv').set_index('Unnamed: 0')\n",
    "train_set = train_set.dropna()\n",
    "\n",
    "test_set = pd.read_csv('/content/drive/MyDrive/ppg/test.csv').set_index('Unnamed: 0')\n",
    "test_set = test_set.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "dZeCfi-ZSwHE",
    "outputId": "5636df96-e421-496a-9493-c7e6142f1b25"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((370, 13), (101, 13))"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_set.shape, test_set.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "eU3FoV8wZiAS"
   },
   "outputs": [],
   "source": [
    "features = ['pk_to_dia', 'pk_to_dic',\n",
    "            'on_to_pk', 'on_to_dia', 'on_to_dic',\n",
    "            'peak_len', \n",
    "            'peak_h', 'dia_h', 'dic_h', 'on_h']\n",
    "X, X_test = train_set[features].to_numpy(), test_set[features].to_numpy()\n",
    "y, y_test = train_set['dbp'].to_numpy(), test_set['dbp'].to_numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "OmMrnSXMYMj5",
    "outputId": "ae4f8a86-a60b-40cb-b123-6c48bc47645a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.034304453707100224\n",
      "[ 25.48010557 -19.564318     4.69677986  18.43585945 -26.60856413\n",
      "   9.29177474  34.57955967 -16.47285566  20.42310903  52.57408263] 11.048954143308158\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "reg = LinearRegression().fit(X, y)\n",
    "print(reg.score(X, y))\n",
    "print(reg.coef_, reg.intercept_)\n",
    "\n",
    "y_pred = reg.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "mBl4_Qd8Z994",
    "outputId": "1892abb1-1381-41c3-ed25-3d44b166a8f1"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "24792.72177656143"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error\n",
    "mean_squared_error(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "zBJonx3SnBpJ",
    "outputId": "372e2430-8694-4962-8f2e-4a2e8cfb444c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8484705022781107\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "\n",
    "regr = RandomForestRegressor(max_depth=100, random_state=0)\n",
    "regr.fit(X, y)\n",
    "print(regr.score(X, y))\n",
    "pred = regr.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "OZdNAcjearOw",
    "outputId": "89c5af7b-6e63-4ef5-bb5d-6cdcc0335544"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "27637.997236108644"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_squared_error(y_test, pred)"
   ]
  }
 ],
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